{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kats 101 - Basics\n",
    "\n",
    "Kats (**K**its to **A**nalyze **T**ime **S**eries) is a light-weight, easy-to-use, extenable, and generalizable framework to perform time series analysis in Python.  Time series analysis is an essential component of data science and engineering work.  Kats aims to provide a one-stop shop for techniques for univariate and multivariate time series including:\n",
    "\n",
    "1. Forecasting  \n",
    "2. Anomaly and Change Point Detection  \n",
    "3. Feature Extraction \n",
    "\n",
    "\n",
    "and after introducing the basic Kats data structure, this Kats 101 notebook provides a basic introduction to each of these time series techniques in Kats.  The complete table of contents for Kats 101 is as follows: \n",
    "\n",
    "1. Kats Basics          \n",
    "    1.1 Initiate `TimeSeriesData` Object         \n",
    "    1.2 `TimeSeriesData` built-in operations         \n",
    "2. Forecasting with Kats         \n",
    "    2.1 An example with Prophet model         \n",
    "    2.2 An example with Theta model         \n",
    "3. Detection with Kats         \n",
    "    3.1 What are the algorithms?         \n",
    "    3.2 An example with outlier detection method         \n",
    "    3.3 An example with CUSUM algorithm         \n",
    "4. Feature extraction with Kats         \n",
    "5. Summary         "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** We provide two types of tutorial notebooks\n",
    "- **Kats 101**, basic data structure and functionalities in Kats (this tutorial)  \n",
    "- **Kats 20x**, advanced topics, including advanced forecasting techniques, advanced detection algorithms, `TsFeatures`, meta-learning, etc. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Kats Basics\n",
    "`TimeSeriesData` is the basic data structure in Kats to represented univariate and multivariate time series.  There are two ways to initiate it, henceforth referred to as \"Method 1\" and \"Method 2\":\n",
    "\n",
    "1) `TimeSeriesData(df)`, where `df` is a `pd.DataFrame` object with a \"time\" column and any number of value columns.\n",
    "\n",
    "2) `TimeSeriesData(time, value)`, where `time` is either a `pd.Series` or `pd.DatetimeIndex` object and `value` is either a `pd.Series` (for univariate) or a `pd.DataFrame` (for multivariate)\n",
    "\n",
    "## 1.1 Initiate `TimeSeriesData` object\n",
    "We will use the `air_passenger` and `multi_ts` datasets to demonstrate how to create a `TimeSeriesData` object for univariate and multivariate time series, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime, timedelta\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "sys.path.append(\"../\")\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from kats.consts import TimeSeriesData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949-01-01</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-02-01</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-03-01</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-04-01</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949-05-01</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         time  value\n",
       "0  1949-01-01    112\n",
       "1  1949-02-01    118\n",
       "2  1949-03-01    132\n",
       "3  1949-04-01    129\n",
       "4  1949-05-01    121"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_passengers_df = pd.read_csv(\"../kats/data/air_passengers.csv\")\n",
    "\n",
    "\"\"\"\n",
    "Note: If the column holding the time values is not called \"time\", you will want to specify \n",
    "the name of this column using the time_col_name parameter in the TimeSeriesData constructor.\n",
    "\"\"\"\n",
    "air_passengers_df.columns = [\"time\", \"value\"]\n",
    "air_passengers_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>v1</th>\n",
       "      <th>v2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-03-12</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>53.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-03-13</td>\n",
       "      <td>0.000</td>\n",
       "      <td>53.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-03-14</td>\n",
       "      <td>0.178</td>\n",
       "      <td>53.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-03-15</td>\n",
       "      <td>0.339</td>\n",
       "      <td>53.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-03-16</td>\n",
       "      <td>0.373</td>\n",
       "      <td>53.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         time     v1    v2\n",
       "0  2017-03-12 -0.109  53.8\n",
       "1  2017-03-13  0.000  53.6\n",
       "2  2017-03-14  0.178  53.5\n",
       "3  2017-03-15  0.339  53.5\n",
       "4  2017-03-16  0.373  53.4"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_ts_df = pd.read_csv(\"../kats/data/multi_ts.csv\", index_col=0)\n",
    "multi_ts_df.columns = [\"time\", \"v1\", \"v2\"]\n",
    "multi_ts_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we construct `TimeSeriesData` objects for each time series using Method 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "air_passengers_ts = TimeSeriesData(air_passengers_df)\n",
    "multi_ts = TimeSeriesData(multi_ts_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'kats.consts.TimeSeriesData'>\n",
      "<class 'kats.consts.TimeSeriesData'>\n"
     ]
    }
   ],
   "source": [
    "# check that the type of the data is a \"TimeSeriesData\" object for both cases\n",
    "print(type(air_passengers_ts))\n",
    "print(type(multi_ts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "# For the air_passengers TimeSeriesData, check that both time and value are pd.Series\n",
    "print(type(air_passengers_ts.time))\n",
    "print(type(air_passengers_ts.value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "# For the multi_ts TimeSeriesData, time is a pd.Series and value is a pd.DataFrame\n",
    "print(type(multi_ts.time))\n",
    "print(type(multi_ts.value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we show how to construct the same `TimeSeriesData` objects as before using Method 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "air_passengers_ts_from_series = TimeSeriesData(time=air_passengers_df.time, value=air_passengers_df.value)\n",
    "multi_ts_from_series = TimeSeriesData(time=multi_ts_df.time, value=multi_ts_df[['v1', 'v2']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`TimeSeriesData` can accomodate time expressed as a variety of different types, including \n",
    "- standard `datetime`, \n",
    "- `pandas.Timestamp`,\n",
    "- a `str` (if in a non-standard format or if efficiency is important, use the optional `date_format` argument),\n",
    "- `int` (i.e. unix time).\n",
    "\n",
    "Here is an example of how to construct a `TimeSeriesData` object when time is provided in unix time format.\n",
    "\n",
    "\n",
    "Here's an example where the time is auto-interpreted from a unix time format. Using this format just requires a couple optional parameters in the `TimeSeriesData` constructor:\n",
    "- `use_unix_time = True`\n",
    "- `unix_time_units=\"s\"` (the default is `\"ns\"`, indicating nanoseconds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     -662670000.0\n",
       "1     -659991600.0\n",
       "2     -657572400.0\n",
       "3     -654894000.0\n",
       "4     -652305600.0\n",
       "          ...     \n",
       "139   -297201600.0\n",
       "140   -294523200.0\n",
       "141   -291931200.0\n",
       "142   -289249200.0\n",
       "143   -286657200.0\n",
       "Name: time, Length: 144, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dateutil import parser\n",
    "from datetime import datetime\n",
    "\n",
    "# Convert time from air_passengers data to unix time\n",
    "air_passengers_ts_unixtime = air_passengers_df.time.apply(\n",
    "    lambda x: datetime.timestamp(parser.parse(x))\n",
    ")\n",
    "\n",
    "air_passengers_ts_unixtime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949-01-01 05:00:00</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-02-01 05:00:00</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-03-01 05:00:00</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-04-01 05:00:00</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949-05-01 04:00:00</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1960-08-01 04:00:00</td>\n",
       "      <td>606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>1960-09-01 04:00:00</td>\n",
       "      <td>508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1960-10-01 04:00:00</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>1960-11-01 05:00:00</td>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>1960-12-01 05:00:00</td>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   time  value\n",
       "0   1949-01-01 05:00:00    112\n",
       "1   1949-02-01 05:00:00    118\n",
       "2   1949-03-01 05:00:00    132\n",
       "3   1949-04-01 05:00:00    129\n",
       "4   1949-05-01 04:00:00    121\n",
       "..                  ...    ...\n",
       "139 1960-08-01 04:00:00    606\n",
       "140 1960-09-01 04:00:00    508\n",
       "141 1960-10-01 04:00:00    461\n",
       "142 1960-11-01 05:00:00    390\n",
       "143 1960-12-01 05:00:00    432\n",
       "\n",
       "[144 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the TimeSeriesData object with the extra arguments to accomodate unix time \n",
    "ts_from_unixtime = TimeSeriesData(\n",
    "        time=air_passengers_ts_unixtime, \n",
    "        value=air_passengers_df.value, \n",
    "        use_unix_time=True, \n",
    "        unix_time_units=\"s\"\n",
    ")\n",
    "\n",
    "ts_from_unixtime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 `TimeSeriesData` built-in operations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `TimeSeriesData` object supports many of the same operations of the standard `pandas.DataFrame`, including:\n",
    "- Slicing\n",
    "- Math Operations\n",
    "- Extend \n",
    "- Plotting\n",
    "- Utility Functions (`to_dataframe`, `to_array`, `is_empty`, `is_univariate`)\n",
    "\n",
    "We give examples of each as follows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949-02-01</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-03-01</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-04-01</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-05-01</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time  value\n",
       "0 1949-02-01    118\n",
       "1 1949-03-01    132\n",
       "2 1949-04-01    129\n",
       "3 1949-05-01    121"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_passengers_ts[1:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Math operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949-02-01</td>\n",
       "      <td>236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-03-01</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-04-01</td>\n",
       "      <td>258</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-05-01</td>\n",
       "      <td>242</td>\n",
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      "text/plain": [
       "        time  value\n",
       "0 1949-02-01    236\n",
       "1 1949-03-01    264\n",
       "2 1949-04-01    258\n",
       "3 1949-05-01    242"
      ]
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     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_passengers_ts[1:5] + air_passengers_ts[1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Equality and Inequality are also supported:\n",
    "\n",
    "air_passengers_ts == air_passengers_ts_from_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_ts == multi_ts_from_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "144"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# length\n",
    "\n",
    "len(air_passengers_ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949-01-01</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-02-01</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-03-01</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-04-01</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949-05-01</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1949-06-01</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1949-07-01</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time  value\n",
       "0 1949-01-01    112\n",
       "1 1949-02-01    118\n",
       "2 1949-03-01    132\n",
       "3 1949-04-01    129\n",
       "4 1949-05-01    121\n",
       "5 1949-06-01    135\n",
       "6 1949-07-01    148"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating two slices\n",
    "ts_1 = air_passengers_ts[0:3]\n",
    "ts_2 = air_passengers_ts[3:7]\n",
    "\n",
    "ts_1.extend(ts_2)\n",
    "ts_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+Btak8eBEH+Ko/Le1qASAEekJfl+TfaR8XVldZHcI9JckyEIIIYQQAXSoogZFqyfV0n0FCLMePBFc8WF3ia/Jydi8dL+v6WgoUtNoDUpMoSYJshBCCCFEAO09XAlAVg9tmuNMWjDG+GaaI9ChGt8s8PQxw/y+Ji/jSPm61vagxBRqkiALIYQQQgTQgfJaAPJSu1+ikBBtQNHqqWmIzOUIFU3tqM52ctKS/L4mOzVxSJSv85ckyEIIIYQQAVRS0wTAiOyUbs93VHw4WF4TqpD6pb5dReNoRqPxP03UaDTgsNLi8AQxstCRBFkIIYQQIoAqGn0NNsbkZXZ7vqPiw+Gq+pDF1B9tqp5otf8zwRq3nbYILl/XH5IgCyGEEEIEUG2rHdXlIDOl+yUWWclxAJTVNoUwKv+59LEkGLsvUdcbg9eJLYLL1/XH8fEqhBBCCCEiRGO7F8XR2uMShdwja3urGltDGZZf6ptbUaIspMca+32tSePBhSEIUYWeJMhCCCGEEAFk9WjQe+w9nh+elQpAbUvkVXzYvLcYgLyU7itw9MZXvq770nZDjSTIQgghhBAB5MBAtMbd4/mslMit+LDzYAUAo3O632DYG8uR8nVu99DfqCcJshBCCCEilt3h5EBZVbjD6BePIYY4o9LjeZ1OC442mu09J9HhUlTh2zg4cXh2v6/NSYxB0erZsq84wFGFnl8Jcn5+PhMnTmTKlCkUFhYC0NDQwIIFCxg5ciQLFiygsbERAFVV+dGPfkRBQQGTJk1i06ZNwYteCCGEEMe1M+55kjP+sCLcYfjNarOjmGJJium9lbTG3Y41Ais+lNZbUVUvk0fl9fvaKcMzAFi7/UCgwwo5v2eQP/nkE7Zs2cLGjRsBePjhh5k/fz5FRUXMnz+fhx9+GID333+foqIiioqKWLJkCbfeemtwIhdCCCHEce29NZspN+aixCRitfW8pjeS7CvxLVHIiI/udZzB66Q9Ais+VFtdYGsm2tT/TXqnTBoJwNZDQ2vGvzsDfmeWL1/O4sWLAVi8eDFvvvlm5/Frr70WRVGYPXs2TU1NVFZWBiRYIYQQQpw4/ueltSiKL1UpqaoLczT+2VdSDUB2sqXXcSaNB2cEVnxodmkwuNsGdO3EghxUZzsHa60Bjir0/EqQFUXhrLPOYvr06SxZsgSA6upqMjJ8U+np6elUV/v+QpSXl5OTk9N5bXZ2NuXl5V3uuWTJEgoLCyksLKS2tnbQL0QIIYQQx4/n3/uC5th8NC2+SbahkiAfrPTFOSy99zbNkVrxoV0ThUU3sLXRGo0Gvb2B6sgrztFvOn8GrV69mqysLGpqaliwYAFjxow55ryiKChKz4vRu3PLLbdwyy23AHSuaxZCCCGE8Hq9PPjODlR9PIunJ/NMEZTVNoY7LL+U1bUA8YzMSet1nMWkBY8Zr9fbr5bOweR2e1BNcSRrB76cJV7roo7+l4iLNH69I1lZWQCkpqZy0UUXsWHDBtLS0jqXTlRWVpKamto5trS0tPPasrKyzuuFEEIIIfry1/9+hN2Sy7w0J+Pyfd9WV9S3hDkq/1Q12QAY3UOb6Q6JMUYUrY6q+qYQROWffSWVKDoD2Qm9r5/uTU68ATU6gfrmyGuC0h99JshtbW20trZ2/v+PPvqICRMmsHDhQpYuXQrA0qVLWbRoEQALFy7kueeeQ1VV1q9fT1xcXOdSDCGEEEKI3rjdHh7/ogTa6nnszsvJTUsEoKZpYOtiQ62+zYVqtxIbE9XruFSL7/zB8ppQhOWXLfsOAzAio/sW2f4Ym5WIomj4YvOeQIUVFn0usaiuruaiiy4CwO12c/XVV3POOecwY8YMLr/8cp5++mny8vJ45ZVXADjvvPN47733KCgoIDo6mmeeeSa4r0AIIYQQx43//fdy3JYsLspoJTYmimGZqcA+6q1Do4pFs1NF6+17k1pqghkqoaSqPgRR+Wd3STVgZFxe+oDvUTg6h5eKa/lqbwnfOX1G4IILsT4T5OHDh7N169Yux5OSkli5cmWX44qi8PjjjwcmOiGEEEKcUF7a0oCiKPzhd75KWcnxsahuF002Z5gj84/No8Wo9B1rdnIc0EJZXVPQY/LXoZpmIJWpo/MHfI9Tp4yBD2vZXdYQsLjCITJWhQshhBDihGe12fHEpjExzo1B75vD02g04GyjxT402hc7tVGYtd4+x+Wm+apcVDdGTkm0iqZ2VGc7OWm9V+DoTUqCBbWtgdKmofGBpieSIAshhBAiImzeewhF0ZCfemwNYa3bHpFd577N6/WiGmOJj+o7vRqW5StuUNNsC3ZYfqtvV9E4mgddVSPa00qjp/dOgpFOEmQhhBBCRITN+3xVsMbmpB5zXI8Luxr5KUtVfROK3kiKue8udNmpiaheDw1tkTPT2qbqiVYdg75PWhS4TAl4vX3PpEeqyP/bJoQQQogTwp4yX+OwKSNzjjluUjy4IrDr3LftKfa1mc5MNPc5VqPRgMNKs31gTTmCwaWPJcGoDvo+w1NiUAzR7DxYFoCowkMSZCGEEEJEhOLaVlSvp8smsZgI7Tr3bfuPlGzLS433a7zG3U5bhCwdaWyxokRZyLD0Pfvdl8n5vioYq7cWDfpe4SIJshBCCCEiQnWbB8XWiMl47GyxxagFQ0zEf2V/uNrX7W9EVopf4w2qC5s3MlKxzXuLAchLsfQ+0A8nTxwBwNaDlYO+V7hExrsihBBCiBNei0dHlLfrprWEaD2KTk9dU2R3Zyuv91WkGJ3nX4O0SFo6sv1AOQCjspMHfa+po4ehuhzsr4ns96s3kiALIYQQIiI49bEkGLrOEifF+rrOHa6sDXVI/VLd0o7qcZGX7t8MslkPHn3vHfdCpajC17Bk4vDsQd9Lp9OibW+gqm3w65nDRRJkIYQQQoRdfXMrSlQcGZauM6qp8TEAlFRHdvOJxnYP2FvR6bR+jY8zRc7SkdJ6K6rqZfKovIDcL17jwKqJCci9wkESZCGEEEKE3Vc7DwAwPDWuy7n0xFgAymubQhlSv7W6FPSedr/HJ5qNKFodFbWNQYzKP9VWF9iaiTYNfpMeQLZFhxqdQGub/38ekUQSZCGEEEKE3baDvjWw449UQDhadkoCAFWNkb2m1Y6eKMX/shSpFt/yikMVNcEKyW8NTi0Gd1vA7jc6MwFFo+WLLXsCds9QkgRZCCGEEGG3t8y3Bnb6mPwu5/LSfRvHapsDl8AFmtfrxW2MI9Go+H1NWoJvZvxwVX2wwvLLH55/F2dcDpNT/Fsa4o/po3y1rDfsPhywe4aSJMhCCCGECLuShjZUt4Px3WwSy8vwbXpraBt8l7dgOVhWjWKMIS8p2u9rspJ9y0kq6puDFVafyqrreWJDI5qWKp6997sBu+9pU0cDsKu0LmD3DCVduAMQQgghxOBZbXZe/GAtb39VxL4mlTl5Ufz73sXhDstvtTYvGrXZ12HuW8zRJlSnjSZ35HSd+7Z1O/YDMDbH/zJpeelJQDNVDeFbOnLlwy9DVA4Pzk/GHB24ZiyZKYmotkYOt0fuh5reSIIshBBCDGG1jS2c+cvnaTKmoxhMqN4MiHGzpqQ63KH1S6vXQDQ9b+hSXO20KuGv9tCTLQcqADMzxvhfBWJYZipwkJqW8Gxke2LZx5QZ8xntLeGqsxcG/P5R7lYahmiqKUsshBBCiCHspQ/X0RybT4qrihtGuVnzk1NIcVTi0Pj/VX8kcBssJPUygan1OLC5/V/fG2r7q5pRvR5mTxjp9zWZKQmoHjcN1tDPstY2tvDHT8vBWst/7gvc0oqjpRi9OA3xEVHGrr+GZlovhBBCCAC+PlAJpPDCT77DmPwsAFJitNR64nC63Bj0kf+rvqy6HsVkJiu65014Rlw4CNwmskArb3GhqI39Wqag0WjA0UqD4gliZN276qEXwZzLL2ZHkRQXG5RnDEuOobTVzP7SKkblZQblGcEiM8hCCCHEEHagtg21vaUzOQbISYxB0erZfag8jJH5b8MuXw3kEenxPY6J0qq4lMhoy9ydJo+eaG//q2wYXVaaXKFNx+qbWykik0x7Mbd854ygPWdCnm9z5drt+4P2jGCRBFkIIYQYwmodWkyupmOOjchIBGDb/pIwRNR/2w9WADAhP6PHMWa9gqqPzGUjXq8XlzGelAHscYvTebBrQ9txbsWXO1C0Os6akNX34EEYnZMGwP6KoVfJQhJkIYQQYojyer04TImkmdRjjo/L8zXb2FMa/gYU/iiq9HWSmzFuWI9j4kxaMEbjdEVeJYtDFTUoxhhyE6P6fW16rA41Kh6bPXTrkNfuKgZg7hT/10sPxPhhvgS8tK4lqM8JBkmQhRBCiCFqW9FhFGMMI1PNxxyfOjofgEPV4auv2x/lje2oTtuRqg7dSzAbURQNpdWRNxu5dlsRAONyU/p9bX5KLIpGy5a9xQGOqmc7y5pQXQ5OmTw6qM8Znp2G6nZS1WwP6nOCQRJkIYQQYoj6bPM+AApHHvtVeXZaEqrDRuUQSUzq7Cpae/c1kDukWHzLK0rC3HWuO1sPVgJQOCq339eOyfYl1Zv3hW45TIUN9O11Qd/AqdFoUOwt1NtCvwlxsCRBFkIIIYYoXwULmDd9TJdzWkcL9Xa1y/FI1KaaMCu9LzFIi/fNkpfVNIQipH4pqmzylXib2P8lC1OPJNW7S2sDHVaPbPo4UvSukDzL4GnH6hl66ebQi1gIIYQQQPcVLDrEYKdNjdyqDx28Xi+eqHiSo3qvcfxNW+bIW89a3uJCsTUSG9P/NcjTxgxD9Xoorg1NN709xeUoUXGMTA3NxkCz1oNdCVyHvlCRBFkIIYQYorqrYNEhwaTgNlhCG9AA7C+tQtGbyEnsvUJFVkoCANWN4WvL3JMmj56oAZR4AzAZDSi2Rqqsodl8uGLDTgBmjAxNXeLEKA2qyTLkmoVIgiyEEEIMQR0VLNJN3S+jyLAYUUxmahoie6Pext2HABiVmdjruPxM31rdutbIW1ftMsQNqMRbB5PXRos7NE1QviryldRbMHN8SJ6XZjGh6E0RuTSmN5IgCyGEEENQRwWLgm9VsOiQl+KbPd4cwuoIA7GjuAqAScN7r8mblZKI6vXQ2Bb6tsy9OVRejWIyD6jEW4cEvReHPjjd7L6tqKYNtb2522U5wZCd5Pv7uetgWUieFyiSIAshhBBD0Ceb9gJdK1h0GJ3jm3HdeagiZDENxIGqJgBmjBve6zidTguONprtkVURYU1Hibec5AHfI8NiQImOp7HFGqiwelTr0hPtCt23CiMykwDYW1IdsmcGgiTIQgghxBC0+aBv5nV+4dhuz08akQ3A/srI/mq7vNmOam8lPTmhz7Eatx2rM7Iqc2w94PsAMn0AJd46DE/zbUDcuOtgQGLqidPlxhWVTGYIGxKOzfN1RzxYFdl/D79NEmQhhBBiCOqoYDEqr/vNVhMLclG9Hkrrgz8rORgNDgW907/KFDqvk3ZvZKUu+yqbUFUvJ08aNeB7jMvztWTesj+4yxDWbN2LojcyPjs+qM852oQROQBUNA5sE2O4RNbfMiGEEEL4pcah67GCBfiqI9DeTE2IqiMMlE2JwqLxryavSePGSXCbW/RXxSBKvHWYdmT2uagiuE1QPtviWw5y8rj8oD7naAkWM6rdSk2rM2TPDARJkIUQQoghxuv14jQl9FjBooPR3UazO3J/1bvdHtSoBFJi/KvgEK0Dt9YY5Kj6p9GjJ8ozuFn6CSNyUd1ODtcHd5Z186EaVNXLglkTgvqcb9M6rTQ6ImtpTF8i91+NEEIIIbrVUcFiVFr3FSw6WHQe7JoQLjjtpx0HSlB0evKS/GtaYTFoQB+aBhf+chniSDYOLvnT6bRo2puoaQvuBsTDzS4Uax1JcaGpmNEhCgc2VR/SZw6WJMhCCCHEENNRwWJaQe+lulJitKhRcThdkbnMYteRChvD0vveoAcQH61HMZhobWsPZlh+6yjxlpc0+A8h0Wo7rd7gJpFNajTxii2oz+iORa/i0nX/Z/Tvtz/jhfdXhziivkmCLIQQQgwxmw5UAj1XsOiQkxiDotWz+1B5KMLqt6LyWgAKslL8Gp8Y41tecbC8Jmgx9ce67fsBGJudNOh7JRpVXEHsfFjf3IpqTiYvLvQzuckxOjBZsDu6rkP+4/u7uP/tnSGPqS+SIAshhBBDzME6W68VLDqMyPB1p9u2vyQUYfVbSa2vesW4fP/aHqfE+5ZXlNYEdzObvzbv933wKBydN+h7ZcWbUEyxVNU1Dvpe3Vnx5Q4URcPUYalBuX9vMuOjUTRa9hR3/aDWro8jxRB533BIgiyEEEIMMX1VsOgwLi8dgD2lkTHj+m2VTTZUr4fRfST6HTISfGtny2uaghiV/4qqfCXeZk8oGPS9RqTHA/DlzgODvld31uz0tfSeO2VkUO7fm7y0eAB2F1cec3xPcTlKVBwFKZG1rhwkQRZCCCGGFF8Fi8Q+K1gATB2dD8Ch6tB1TuuP+nYPtDf7uuT5ISslHoDKBv/qJgdbebOvxFtc7OATvAn5voYa2w8Gp/PhrvJmVJeDUyaPDsr9e9OxhKZjSU2HVRt3A1BY4N8HpFCSBFkIIYQYQnwVLKL7rGABkJ2WhOqwUdlsD0Fk/dfqUtB7/N9wl53qWzJS0xwZTSeaPDqiPIGJZcbYYQDsqwhOx7kKG+jb6zDoQ19Hevww32bSwzXHflD7qsi35GLe9DEhj6kvkiALIYQQQ0hHBYvCkdl+jdc6Wqi3R2YN2nbFSIziX5MQgGFZvvWzDdbISPidhniSjd6A3Gt4dhqqs52yxuBUmbDp40jR+/9nHUij8zJRvR4qm459bftrrKh2K+OH+/d3OZQiqx2NEEIIIXq1/XA1kMypU/xrbRyDnTbVENygBshriCVecfg9PikuFtXtoNEWnkTvaB0l3nJjApPQajQatPZm6pTAf5jpXOtr9v/POpB0Oi20N1OvObbOc7Vdi0lpRKOJvPnayItICCGEED2qaGxHdbsY42flhwSTgtsQ2sYQ/qhvbkUxxpAa28/k3WGjxR7chhr+WPGVrzTZ+NzkgN0zRrFjVQPfKXDFBl+sM0f1Xjc7mAweGy0upfO/vV4vDlMiaabAzMAHmiTIQgghxBBSZ/Og2Jv9nnXLsBhRTLHUNkbGxrYOO/aXApCd2Pda6qNpPXbaIqAq2Ovr9qF63Fx33ikBu2dKlILHFI/X27+k8anln/DyR+t6PP/ZDl+Zv/NOnjSo+AYjRuPGrpg6/7ujG+RoP9bSh4MkyEIIIcQQ0urWYvT6v7EtL8XXfGLTnkPBCmlA9pZWAZCf5l8XvQ4GXNi9/lW9CKa9Vj3RbRVkpiQG7J7ZCdEohigOllX7fc3GXQf43WcN/OLNnptt7KpzommpZER2eiDCHJAEo4LX8E0yvOrrPQDMGB15649BEmQhhBAnIK/XG7SGDMHm0EZh0fq/xGB0jq/E1s5DwSkfNlCHKn3VGkbnpvXruiiNF5cm9N3gjrZ6yx7U2HRmZkcF9L4FGb4PCxv3FPt9zY2Pf4BiMOG1ZLBlb9cPQTa7g7aoNHKjunaxC6XUWCOKMabzm4yvD/g+IM0vHBfOsHokCbIQQogTzq1/epFZv/+Ef72+Ktyh9Ivb7UE1xZEc4/8M6qQRvhm6/ZXBKR82UCV1rQCM62cFgxg9eHWBTUz766n3vgTgpnNnBfS+k4b71pXv+FZDjZ48tPRtmmPzSbUV++J6t+syi1dXfomiNzF3TEbA4hyI7CTf7PH2I10dD9TZUG1NYZ3V7o0kyEIIIU44m0pbUPRGHlpdz6srvwx3OH7bV1KJotOTEe9/gjixIBfV66G03hrEyPqvpsWO6nJ01jb2l8WoBUNMv9fpBtKXZW0ordWcOnVsQO87c/wIAA5U9v3tRm1jC0s2NqJpqWLVwzeitjWy5mDX6977qgiAa84KbDLfX8PTfe/znsO+meM6l55od2Q2sAFJkIUQQpyA6j0mdC3l4Grnp28dZNVXO8Idkl92HCwDYHg/1u2ajAZob6bGGgE7247SYPeiOFr6XeIrIcaIotVRVd8UnMD6UFHbgC0mk1HmwC9ZyExJRLVbKWvqu87ztX94CWKS+MXZwzFHm8jWNtOgT8VmP7aU244aB5qWKkb52c47WEbl+JbSHKpqwO324IpKIjNa6eOq8JEEWQghxAmlta0dT0wyoyxenrl2GgDXP7eJjbsOhDmyvu0rrQFgVE5qv64zuttodkfWr3yrW4vR2/+GH0mxvkoIhyvrAh1SF93NUv/jjU9RtHouOzk4LZv1zmYaHL0nju+s3sQuTwYZ7cXctGgeAAsmZKEYo3nhgzWd42x2B1ZTKjmm8NQ/PtrEghwAyupbWb+jCEVvYkyGJcxR9Syy/rUIIYQQQfbRl9tRtDqm5KdwxowJPLJoBOiiuOzxzzhU7n/1gHA4WO37Cn1CP9ftWnQe7JroYIQ0YA6tCXM/Nht2SIuPAaC0Jrhrqmf+6HEm3/EPnK5jZ94/2lmJarfy3XMCV97taBaNizZNTI/nvV4vd7+4AVztPP+TizuP37LoNFSPmzfW7+s89vonX6EYojh1TPjX+aYnJ6A6bVS3Ovh8q2/Zx+yxuWGOqmd+J8gej4epU6dywQUXAHDo0CFmzZpFQUEBV1xxBU6n76sGh8PBFVdcQUFBAbNmzaK4uDgogQshhBAD8dk230zxGdN8neguOWMm95ySgBqbxm+f+yCcofWpssnXJGRUbv82XKXEaFGj4nC7w99gA3xJnmqykBjV/3m6jKQ4AMpqgluFpMYbQ6slnyt/+0znMafLTRWJpHrqfEtXgiA3Xo8ak0h9c2u3599dvRlnXA7z0x0UHPX3IDMlEVNbBXtbvmmS/O4GX1vyq8+cGZRY+0vjaKXRrrLloO+D6PwZ48McUc/8/pv517/+lbFjv1mMfs8993DXXXexf/9+EhISePrppwF4+umnSUhIYP/+/dx1113cc889gY9aCCGEGKAdZY2oLgenHbXB6gcXn4HqsHGwti2MkfWtrs3XJESn618d4NzEGBStnh0HSoIUWf+UVtej6IykWUx9D/6Wjqoce8uCt8SiI4FX3U6+dqR1Vjv5z0drUaIsnDmuf6Xp+mNCbjKKouHTr3d3e371joMAXHTyhC7npqYZ8Foy2LrvMADbqx0ordX9rhQSLCbVgdWj42CDA9rqA1pDOtD8SpDLysp49913uemmmwBQVZVVq1Zx6aWXArB48WLefPNNAJYvX87ixYsBuPTSS1m5ciWqGvi+4kIIIcRAVNgU9O11GPTfzLRpNBr0jkZq+78kNqRaPBqMHv+bhHQYleVrh7xpX2QkyDuPbDbMSep/C+xpY4ahOtspqul+hjUQOhL4aVF1aNrqeejTCvYdruC/X+xC9Xq49aLTg/bsOROGA7B+V3G353eWNqB6PZw+vWv94KtOnwzAU++uwe5w0mpKIdvQ/78vwRKr8+LURdHgMWL2RFZVlW/zK0H+8Y9/zB//+MfOnab19fXEx8ej0/l+uGRnZ1NeXg5AeXk5OTm+hdg6nY64uDjq6+u73HPJkiUUFhZSWFhIbW1tQF6MEEII0Ruv10u7IZE0g6vLuXitC1svaz8jgUMTTayu/8skJo3IAmD34chYY723xBfHiMykfl+r0WjQ2xuosgU6qm9sP+Brgz02K5E/XzoeDDFc/Ic32dOiJ8paTm56ctCePWfKGFSPm13l3S8hKW1xo2mrIzama6m/C0+dhtrWyOoDjbz52dcohmjmjArebHd/JUVrwRSHJzqZ7NjI3gbXZ3TvvPMOqampTJ8+PaAPvuWWW9i4cSMbN24kJSUloPcWQgghurNpzyEUk5kx6V1nLrMsvrWfza2RuczC97V/HMnR/W+zXDjONyt5sCYy6s4WH9lsODp3YJvHEnUu2rT9n332176jEviL583krJRWrJY8vJZ0CjP7vyykP8zRJjS2espaui/L10I0cUr3s8IajYYsbTMN+hTeWLsLgKsXRMb6Y4D0uCgUrQ5Fp2d8dv9ajIdanwnymjVreOutt8jPz+fKK69k1apV3HnnnTQ1NeF2+968srIysrJ8n06zsrIoLfV98nK73TQ3N5OU1P9PiEIIIUSgrdjoW9d58riuu+dHZyagKBrWbS8KdVh+2V9a1e8mIR0SLGZUWxOVLeFtN9yhotH3IWTCiJwBXZ+XYESJSaSmITgJ/6FvJfD/+tl3SbAWA3DD2TOC8syjxSvttNC16khzaxvemCRyLbpurvJZMD4TxRjD+gYTSmsNEwsip1JEXkpc5/+fPS4/fIH4oc8E+fe//z1lZWUUFxfz8ssvc8YZZ/Diiy8yb948li1bBsDSpUtZtGgRAAsXLmTp0qUALFu2jDPOOANFidxC0EIIIU4cX+/3dfE696RJXc5NG+nbyLRhz+GQxuSvrft9k0/5qXF9jOyewW2l0RkZX2vXtDpR7VYSLOYBXT8h17fEYdXGXYEMq1NHAj/xSAKv0Wj4+MFr+fUpZs6Y0XVzXKDlWHR4Y5K6fJvxyde7UDTaztffnRsvPBXV60GJjidTH8R1KANQcGQtvKp6OTOCK1jAIOog/+EPf+CRRx6hoKCA+vp6brzxRgBuvPFG6uvrKSgo4JFHHuHhhx8OWLBCCCHEYBxscIC1rtvd83Mm+8q+7S4NfgOKgdhX6vvaf3TOwNaUWrRu2jX9n30OhiaHitY58E1aJ4/3LRn5am9wNh36EvhW4mK/WZOeFBfLDRfODcrzvm1CbjKKRsunm46tZLHuyMa9k3qZfc1NT8bUWgHAKSP711Am2MYN83XzU6x1A/5wFCo9z9F34/TTT+f0008HYPjw4WzYsKHLGJPJxKuvvhqQ4IQQQohAalSjiaP7xCw7LQm1vZnD9sgsZVFc3QQkMXHEwEp2pcZoqfPE43S5j6ngEQ5tqh4TA+/udsqU0aivHWJ3DxvZBqvJCVo1fFUWThqXz0vFVazfVcyiuYWdx30VLIzdVrA42vQMA2ta3VwTQeuPAcbkZ6GqO4knsma2uxMZ37UIIYQQQVbb2ILXnEx+vL7HMUZnC/XO/m+CC4WyRhuqx8XovMwBXZ+fbEbR6tlWFP4lJC5tNBb9wEvARpuMaGz1lLcGp/GJTTUQTfjWa58+fRyq18PO0mOrgPVWweJoT/38GpZcnM/kUXnBDLPfok1G0u0lXDAp/J39+hLej5BCCCFEiHy4fhuKomHa8J6/dk4yeKhQ40MXVD/U2zwo3v43CekwKiuZ9+s8bCkqoXDciABH5z+nyw1RFpK0g6sWYqGdFoKzZMStj8GiBLdTX29iY6LQtNVTqhxbyaKZaOKVvmdfo01Gzj5pcrDCG5Qv/3p7uEPwi8wgCyGEOCGs3lEMwILCsT2OyU0woUTHU1HbEKKo/Nfi1mAYQJOQDpMLfEszdpfUBCqkAdl7uAJFoyVzANU4jpZt0eGNTsJmH/hSje7Y7A4wxZIS0/M3DaFgwUaT+s2fUXNrG2ofFSxE4EiCLIQQ4oSwq7IF1WFj9sSRPY4Zm+0rS7pmW+SVerNrogbUJKTD9DHDADhY0xKokAZkd7FvA1lOysCqcXQYm5WAotXxxeY9gQir066DZSiKhsyErmXWQinnyAcAq823Jt6fChYicCRBFkIIcUKoatdgtNd3doXtTuEYX83YTftKQxWWXwbTJKRDXGwMqq2RqjDXQt5f7uueW5A5uERv5pH3at3OQ4OO6WgdCXx+WngbWYzLTkTR6vh8s6+SxdqdxUDvFSxE4EiCLIQQ4rjndntwRCWTEeXtddwpk0ahql72VIRv/Wl3DpZVo+gMZMQNblmC0WWl0RXeX/2Hq5uAb0p+DdTp03xLZXaUBLYs3/5y3/0KssLb5Xf2ON8Gu7VHPgDsKmtA9Xr6rGAhAkMSZCGEEMe9ddv3oRiiGJ/V+9f6CRYz2Bopa4qMjnMdBtskpINF58GuCe/SgcomG6rqZUx+1qDuk5oYh9rWQElTYNcgH671LUEZP3xw8Q3WvOnjUFVv5wcAfytYiMCQBFkIIcRxb+XXewGYMz6/z7HRHiuNnvBu0Pq2vZ1NQgbX+CHNrEONisfuCN8HgDqbG9pbMBkNg75XjKeVBndg36vqlnZUj4uC7PCWIkuwmFHa6ilpcgEdFSwGvklT9I8kyEIIIY57mw/VoHo9nN1Ni+lvSzWByxiP19v7coxQKj6yLGHCAJuEdMhLNqNodWwNYy3kZqeC3j24Em8d0qPBZUrC7Q5cPeR6mwfF3jLgcnqBFKu20eQ1dVawyJEKFiEjCbIQQojjXnGTC01bPUlxsX2OzUuKRjHGsL+0KgSR+aesoQ3V4x5wk5AOY7J962q3hHETYjsGohRXQO41Ks2CYjCxeW/gNuq1uDUY3JHR6S07VosnJon3122TChYhJgmyEEKI416rasSs+pf0TMjzJZFrt+8PZkj9UmfzgL150C2iO2oh7ykNXy1kjz6G+MGvrgBg6gjfB4YvtgauLJ9DMRGjDU6Hvv4al5WAotXz0qfbADh5wrAwR3TikARZCCHEcc3r9eIxxZEcpfg1ftZYXxKyeX95MMPqlxa3BmMAZjWnjRmGqno5VBueWsitbe0oURZSzIHJkOdOGw3A1uLqgNwPwGuMJcHk39+VYJs11lfJYnuzHtXrYe7UnpvciMCSBFkIIcRx7WBZNYohmpxE/6o3zJpQgOpxU1TVHOTI/DfYJiEdYmOiwNZMZWtgljj0144DvqUdmQkxAbnfqNwMVLuVg7WBWdNcUduAYogizWIKyP0Ga96Rkm5qbJpUsAgxSZCFEEIc1zbsOgjA6KxEv8abjAY0tgYqrZHxNbuvSYiFxKjA/Mo2uq00hakW8t4S37ruYQFqwqHRaDA6GqlxBGZD3fYj5fRykvpeqx4KKQkWsPrKvEkFi9CSBFkIIYRfPly3lT3FkbPswF/bD1UC36y/9YcZG81eY7BC6pdDFTUoOiOZ8YGZPYzXe3BoAzOD21/7K4404cgOXBOOFKMHu35w9aE77C3xLdUYkZEUkPsFQqzXCiAVLEJMEmQhhBB+ueWlbVz0x+XhDqPf9lc1ATBrfIHf16RHa/BEJQa0fNhAbS0qASA/JTBJYDBrIR8oq+q1PF5xjW/ZyvhhgWvCkZ8UjRJl4UDZ4KuOHKpqAGBUbtqg7xUombG+2XGpYBFakiALIYToU0VtA0pMIu2WPN5ZvSnc4fRLebMDtb3Z93W1n4anmFH0RrbsKw5eYH7ae6TixMgAzbrmJceiaLRs2hO40mgANQ3NnPHIai75zdPdnvd6vayrcKO01jA8O3AJ6ORhvuYpn23eO+h7lTf4Zmsnjcwd9L0CZXKubzb7jGmjwhzJiUUSZCGEEH1as+2bMlq/X7YujJH0X4NTg8HZ2q9rJg3zdVH7cufBYITUL8VVjQBMHJETkPuNzvbNRG4pCmwt5E837UYxRLGpLZFdB8u6nP/zSx/gsWRy4UgTGk3g0o85E33fDHy9r+sze1JV10j+Hc/yi3++dszx6lYnqqPNr3rZofLALRfx8BmJnDlzYrhDOaFIgiyEEKJPm440ljA0l1Kmy2B/SWWYI/KfXRNNnM7dr2tOmjgCgG2Hwt8spKzRhur1MDZAyxKmjvLNju4pqw3I/Tps3Ov7O6IYTNz++Ftdzj+5pgS1rYGHbrkooM+dOb4A1eVgX5X/pev+tmwVmFP477aGY5aENDlUtI7+fZgKNoNex5VnnRTuME44kiALIYTo094K3yzmby+ajKIz8j9PvxvmiPxjsztQoxPIiO3fBqdJBXmoLjsHaq1Bisxn18EyFvz8Hxwq77mOb12bG9oH3ySkw5RR+aheD4drA5sI7q1oRFW9pNmKOajJZN22fZ3nnlr+Cc64HOZneTFHB7aEmk6nRdveQJXN/9bgH+zwffDxWDJ5/v3VncfbVD0mHAGNTwxNkiALIYToU1mzE7WtgSvPOonolsNsaDRhs0d+IvH17oMoGi3DUv1ffwwdSVcj1W3+J10D8c+3vqBIk8tZD7xJbWPXGdD9JZVUe80Y3YGp8wv4EtT2wNdCLmtxobQ18K/bzwPVy4+fWtF57tEPd6O2N/Pn2y4J6DM7xGkctOJfnevGFiv1hnRS2opRnTYe/3B75zmXNhqLXg1KjGJokQRZCCFEnxrdOqI9vtnU607OQ4lO4P5/d/0aPdJs2uurADF+AFUJ4jQOrIp/SddAdcziOmMzmHvfC1ht9s5zuw6WcdbD76GaYvnhvOEBfa7J3UazKzC1gzs0eQzEeNuYMnoYY7Q1VEXl8s7qTbzy8XraLHnMTmgnwWIO6DM7ZMXqUGOSjvnz68njr61CMZj47ikF5FNLtSGL/SWVOF1uiLKQHC3l1IQkyEIIIfzgNMSTbPTNpt595TkorTW8tqMxzFH1bXepb53t9DF5/b42M1aHNzoxqDPl1VYXalsjCzOs2Cx5nPbzp3G63GzfX8L5f/4IT1Q8P58Vyw8vPyugz43Xe7DrAlcL2e324I5KJO3I54knfvgdcNr5xX/W8dCbX6M62nj09sCuPT7a6Mx4FI2W9TuK+hz79uZSVLuVm79zOvdcchKKTs+vnn2f3YfKUTRaMhOC+6FIDA2SIAshhOjVgbIqFJOZvCRf4qDTaTkjR4vbksXz730R5uh6V1xnRXW7mDSy/wlyQZoFRavjyx37gxCZT5NLg9Ft5bG7rubk6GoazPnM+cm/uPDRlXiNFn51WiK3X7Yg4M9Nj9VDVJxfM67+2LKvGEVvoiDVV/1hRHY6hbFNtMTm02TOZ1JUI5kp/nUyHIipBb4NjF/u6r10ndVmp0qbQrpaR7TJyHmnTMXYXMq6Gi3bj7TBzksNTL1pMbRJgiyEEKJXq7f6NluNz/2mDu/vb1mE6mjjL+9vC1dYfqlu86BpbxzQBreOpOurPcUBjuobdk00Fp2vGclLv76BsZRSE52PajBz/xmp3LRoXlCem5fiq4W8eW9gaiGv3X4AgMlHyuMB/OPHl6O2N6O67Dz6g4UBeU5PTps6BoAdJXW9jnty+acoxhgWTv2mZN7Fk5LBnMy/PtoKwIjMwHX5E0OXJMhCCCF6tfVABQAzjlqmkJoYxwhtHXWmbOqbI6ss1tFavAai1fYBXTtn0kgAdhwObDm0Dk6XGzUqnpSYb9YCv/vQLZyb3MhfF+Vz3QWnBeW58M2a7K92FwfkfluPlMM7ZdI33QpTE+O4b146P5hspCA3IyDP6UluejJqezOHG3p/r1/bcADVaeeOS+Z3Hrtv8QWo7c2U6HytyMcPD1yXPzF0SYIshBCiV0VVzaheD7MnjDzm+Bnjs1G0Ot7+YnOYIuub22AhaYBVxYZnp6HarRTX2wIb1BE7DpSgaHXkJX2zcU2j0fCPn36X75w+IyjP7DC/cCwAXx8ITJ3ng7VWVGd7l6Ust3znDP7n2gsC8oy+GJ0t1Dt63njodLkp9SaQ5KwiLvab9dfmaBMToltRtDpUr4fReZmhCFdEOEmQhRBC9Kq8xY1ia+hSv3bRqVMAWLU1/N3munO4shbFZCY7fmAZskajQe9sptauBDgyn45OdiMzk4Jy/94U5Gag2prYXxeY5L+6HXT2xoB2yOuvZKMHu6Hncn7Pvvs5SlQc50xI73Lu19ecgap6A1pvWgxtkiALIYToVbPXgNnbNZGaWJCL2tbAzqrA1egNpA27fIn7qMyBbw5L1LmwaQNX7eFoe0pqAJgwPDwzljHuZupcBr/H2x1OPvt6V7fnbFozCVpnoEIbkLzEKJSoOEqqul+H/PLqPahuJz+69Iwu52ZNGEmKrYQ4r//d+MTxTRJkIYQQPfJ6vbhNiaT2UPkqUW2hXg1ObdvB2nagHIBJIwaegGbHGVBikmhsCXxHvUM1zQBMGzMs4Pf2R1aMgjs6Gbuj98TW6/Xyv0+9ydi7X+TaVw7w2qoNx5xvbLGiRieQHacPZrh9mpCbDMDnm/d0Oef1ejngiMXSXkF6ckK3169/9AdseezWoMYohg5JkIUQQvRo58EyFIOJYcndJ8FjUkxgTmZ/SWWII+vb/kpfneaZ40YM+B6jM33J1OotewMS09EqWxyo7S2kJPSvy1+gTMhOQNEZ+KKbhLLDU8s/YfQdT/Lsfj2ggtfDkg83HTPm8817UBQNY7OCV8bNHzPH5AOweX95l3P/XbEeJSaRM0f1vJxFp9OGdYmIiCzyN0EIIUSP1m7z1QCekJfa7fnTJuQD8MbnkbdRr6zJjmpvJTtt4Gt8p43ylQP7el9poMLq1OhQ0LvCVwHk5Am+mevPtnVf5/niXz3JA+tsOLUxXJDazO5HvktsWwV7bdF4vd+04P7qSLfCwtG5wQ+6FydNGoXq9bC3oqnLuZc/34Hq9XDHJaeHPC4xNEmCLIQQokfbDvlmhmeP777V8SXzClG9HtbsKQtlWH6pdyjonYNbU3rq5FEA7C6rD0RIx7ApJswaV8Dv668zZ4xH9XrYVtx9GbtNjQZMLSVsemARf7/7akxGAwtGJ0JMEi99uLZzXMefTUct4nAxR5tQ2uopb3V3Obe7wYveWsWI7K4b9ITojiTIQggherS/phXV7WL62O4T5NTEOLTWWvY3dE1Kws2miSZOO7gEND05AdXWREljYDrOdfB6vXhN8aREhe/XcILFjKatnsPNXd+77ftLwJzM9AwTSXGxncfvuuJMVLeLpau2dx4rbXKitjWEbanI0WJVG81e4zHHKmobcMRmMjpODVNUYiiSBFkIIUSPqqweNO31vZa+StPbaTUkHvO1e7j5mnAkkBrTc11cf5ncrdQ7B3+fo+0vrULRG8lK6GH3Y4jEKzaala5VOl5ZtRGAc6YXHHM8Nz0ZS3s5++xm3G5fB8AGt55oT+A3MQ5EeowGT1RiZ2wAz763BkWj5fzpA1+LLk48kiALIYToUQsmYul99nRydhyKKZa12/aFKKq+bd57CEWrY1hKbN+D+5Bs8ODopb7uQGzaexiAgozuKyqESn6CAcwp1DQ0H3N87b5qVLeDi7ppWHL22BSUmERe/HANXq8XlzGeVFNkzM4WpMWi6I18veeb2twfby9FdTm45uyTwhiZGGokQRZCCNEtt9uDNyqRDHPvs6dnFfrW6b67bkcowvLL13t8CejYnJRB3yu3j/q6A7Gz2Le2e1x+eNfETh3m23z50Zfbjzl+2KbFZKvp0hwG4O4rzkR1O3nuk53sKa5AMcYwLDk4taL7a9IwX0vrtdsPdB471G4kpv3Y7nlC9EUSZCGEEN36es9BFJ2BEam9z8Kec9JkVJeDrw52v9krHHYdacIxfXReHyP7Nj7HV193zdbAzZAfrPbN2E4PUw3kDvOm+j7crNt1uPNYfXMrrpg0RvQwaZ6ZkkhcewX7HbF8ttlX/m5SfvdVTkJtzmRfO/Tth6sB2LrvMGpsOpPTB9hvXJywJEEWQgjRrfU7fF9TTz4yK9eTaJMRo62GEmtwWjIPxKHaVlSvJyBNODrKl20qClyljoqmdlSnjZxBlKALhJMmjkJ12tld+c0Si1c+3oCi1TF3fE6P1507PhUlJoFnP/PVUO6pykmojR+ejeq0cbDW193xuQ/XA3DpnAnhDEsMQZIgCyGE6NaOI7Nwsyf0vbkp16ziiEnFZncEOyy/VFndKLZGTEb/Wyn35JTJo1FVL/sqmwYf2BH1dhWtvTnsjSl0Oi16ez0Vtm8+3Hy81ffB6PIzCnu87q7Lz0R1O6gy5aC6nRQOohlLIGk0GvT2Rmraff+9uqgO1d7KotOmhzcwMeRIgiyEEKJbB2qtqE47Ewt6nknsMHNEKorOyPtrt4Ygsr41e/REedsCcq/YmCiUtgbKWwJXs9iqGolRIuPDRKrBRbshobMKyZ46F5qWKoZlpfV4TXpyAgn2ShSNFq2todcqJ6EWr3XRpjXj9XqpIo4kdz06XWCrkIjjnyTIQgghulVjA529wa9ZzgtO8n2FveLryKhk4dLHkmQMXGWFaG8bTR59wO7nMVpINEbGkpSRqTEoplh2HizD7fZgNSaTaey77vN5E3wbDOM0ga0RPVg58QaUmCRe++QrlOgEZg+LD3dIYgiSBFkIIUS3rJpo4jROv8bOnjgStb2FrWXNfQ8epEdeep+5dz/B6i17upwrqapj5o8eR4myMDZ98CXeOqRGgcuYEJBaz2XV9SjGGDLjI2Pj2MyRWQB8/NUu3l+7BcUYw8xhfa+NvuuKBah2K6NTIuN1dBiblQjAX9721XL+7pk9LxURoieSIAshhOjCZnegRieSGevfV+cajYZYVyPVruAnS8+sOchhQx7XvLiHM376BPsOVwDwj9dWcuqD71NtymGKrpzHfnxlwJ45LDkGxRjNnuKKfl338kfr+NWS14859vWeQwAMT4sLWHyDsWDmeAA27q/knfW7ALjo1Ml9XpeSYOHze87g2Xu/F9T4+mv6KN+SoDJtBljrOHny6DBHJIaiyFk0JIQQImKs3bYPRaNlVIb/SVxBoo7NrlSq6hpJTw5eA4xWxUx0y2ESjSoH9Dks+OtaEpzVNMbkonjdPHRGElefszCgz5yYl8on27ys3V7EuOHZfl2zYv027nm/DPRGTlm7hXNOngJ01EDWMDa35zW+oTQqLxPV9gn72214vaBqtJziZ1KZlzH4OtOBdtrUMfBRLYreSLa3MtzhiCFKZpCFEEJ0sXqbr9HC7HH5fl9z6tgcFEXD659+HaSooKquEczJjEs2sPrR23nq4nwSnNU0mfPJdZbw1QOXcPU5pwT8uTPH+crFbTngX8J1uLKWm5//GrxucDu496U1nef2VzYAMG10fsDjHKgYdzN1LgO1qplET2PYq2sMRkqCBbXN92d8+tjeSxQK0ZOh+y9ACCFE0Gwv8TX9mDd9nN/XXDjH97X82j2lQYkJYMUGX7e+qcN9jSkWzJ7Elr/fzvqfnswXj95OSkJgW0J3mDm+ANXj4kBNS59j7Q4n5/7uVdToBH49P4PpMU00mfN56QNfklzWYEN1OxiTnxmUWAciK0bBbU4DczIT0qPDHc6gRXusqKqX686R9tJiYCRBFkII0cXhRidY6/qVcI7Ky0Rta2RfjS1oca3fXQLA3CmjjjkezCUdAAa9Do2tgYpWT59jz7vvKWyWPC7MsHHjwtN54s5LUdtb+N1b2wCos3nRtIe/BvLRxmXFo2h8pdDOLRzVx+jIN2+EhRxnCQW5MoMsBkbWIAshhOiiwWPETP/rCFs8zdQRvI16uyubUb0mZk8YGbRn9CSWdlrV3l/bHY+8xEFdHsPdh/n73bcBvuR9XpqTT1ty+dt/P6LFqyeK9lCE7LdTJgxjeVU9qsvBd04f+lUfnvjJNeEOQQxxkfPxVQghRERwutx4opPINPe/Tm9+vA6POYXm1sA06fi2SpuC3h6exg8ZZi2e6KQeuwXa7A7eLjMS1VLCew/edMy5x+68HNrq+dtnxbj0ZhIMgavRHAgLZk5A9XowtVcTbTKGOxwhwq7PBNlutzNz5kwmT57M+PHj+c1vfgPAoUOHmDVrFgUFBVxxxRU4nb5amQ6HgyuuuIKCggJmzZpFcXFxUF+AEEKIwFq3bR+KzsCYzPh+XzttWCqKRsuHX24PfGCATR9Hij5wHe36Y/qwFBSdnnfXbOn2/Ptrt6IYTFw0KbVLi+vYmCguKjDgtmShRMWRbhl8C+xASrCYGeEt49LJqeEORYiI0GeCbDQaWbVqFVu3bmXLli188MEHrF+/nnvuuYe77rqL/fv3k5CQwNNPPw3A008/TUJCAvv37+euu+7innvuCfqLEEKISHSovJrhtz3FH55/N9yh9MsX2/YDMHtMbr+vPbNwDACfbT0Q0JgA9hSXo0TFUZASnk1kF548EYCPe+gWuHJzEQDnzR7f7fk/3HYpSmsVAMNSI6MG8tFW/ek2Hvz+JeEOQ4iI0GeCrCgKZrMZAJfLhcvlQlEUVq1axaWXXgrA4sWLefPNNwFYvnw5ixcvBuDSSy9l5cqVqGpkfZUkhBCh8PLKr/BaMnhiczvvrN4U7nD8tq24BoAzCv2vYNHhpImjUB02dpQHvqPeyq98TSxmHOn8FmqzJhT4ugWWd1/JYmtpE6rdysmTut/kZtDruHlmKqrqZc6EYcEMVQgxSH6tQfZ4PEyZMoXU1FQWLFjAiBEjiI+PR6fz7fHLzs6mvLwcgPLycnJyfF1sdDodcXFx1NfXd7nnkiVLKCwspLCwkNra2kC9HiGEiBgb9x+pmetxcsfL29lfMjSaFhQ3OlDbGgZUGUKn02Ky11PZHvgtLhuKfL9n5heODfi9/aHRaLC4Gqh2d79Rr8ppIMZZ32t1il8svpBPfzST75w+I1hhCiECwK+fYFqtli1btlBWVsaGDRvYs2fPoB98yy23sHHjRjZu3EhKSuR14hFCiME62OAEay0PnpOLarJwwcNv9rjBK5LUuw2YPa0Dvj4z2osjKhm3u++SaP2xv6YN1d7K2GHhmUEGKEgy4DWnUlHbcMzx5tY23DGpDIvre/PgsKzI6KAnhOhZvz7ix8fHM2/ePNatW0dTUxNutxuAsrIysrJ8P7CysrIoLfUViXe73TQ3N5OUlBTgsIUQIvI1YiaRNr577hwuy3dht+Ry9v88Fe6weuV2e3BHJZER0/8KFh0m5iSgGKL4YsvgJ1OOVuPUEeUMb5e3OWOyURQNb3527JKZt1dvRtHqmFkgya8Qx4M+f8rU1tbS1NQEQHt7OytWrGDs2LHMmzePZcuWAbB06VIWLVoEwMKFC1m6dCkAy5Yt44wzzkBRBv6DVgghhqKy6nowJzMiyVcy6093XM4YtZRSYz63//nFMEfXs/U7ilD0RkanD7wj3dxJIwBYuWlvoMLC7fbgNCWRHhXePS0Xnz4NgM93Hj7m+KojmxIvPGVSyGMSQgRenwlyZWUl8+bNY9KkScyYMYMFCxZwwQUX8Ic//IFHHnmEgoIC6uvrufHGGwG48cYbqa+vp6CggEceeYSHH3446C9CCCEizQfrfV3TZoz8ppPXWw/chK6lnA8OBK/T3GB1VLCYOSZnwPc4a9ZEVI+bTQdrAhUWX+06gGKIYmxGcFpJ+2tYVhpYa9ldYz/m+M5KK2p7M1NG5YcnMCFEQPXZSW/SpEls3ry5y/Hhw4ezYcOGLsdNJhOvvvpqYKITQoghavXOEiCZc2dP7Dxm0OsYE6ey3Z1GfXMrSXGx4QuwB9sOVQPpzJve/woWHWJjotC21VKCN2BxfbJ5L6Bh1gBKzwVastJGrXJsmbYatwkL4V3+IYQIHPmXLIQQQbC32jejOH549jHHTx6TiaLV8eZnX4cpst4dbLCj2hrJTU8e1H1S9A5adYGr9bvlYDUAZ87ovsZwKI3PiEGJSWDL3kMA1DQ04zWnMiJBH+bIhBCBIgmyEEIEQY3LQIyrqcuM4sVzfWtYV245GI6w+lTv0hPt7r7Ob3+MTo1BiY5n18GyAEQFBxscqG31ZKeFf9P3GZOHA/DWGt8ymjc//xpF0XDymPBV1xBCBJYkyEIIEWBWmx13TAp5lq4/YsfkZ4G1jt017WGIrHderxeXKYmMADSqO2WsbynEB+v733J66bufdymj1uAxEuuxDj6wAPjOadNRPW7W7fPVtf58h2/D3sI5U8IYlRAikCRBFkKIAPt4w3YUrZ4ped0vU0hWrDQokddq2LcRzsSotMFvhDv3ZF81hw1FFX5fY7M7OP0nT/CbL1qZ85s32HmgtPO4JyaZ7NjI+JUVFxuDvq2a4hZfRY3d1TbUtnrfhx8hxHEhMn7aCCHEceSzIyW/zpjafcvhCRlmlJgENu2JrGUWn28pAqBw1OATvdz0ZLDWUVRn73swsL+kkml3/5tifR4pbcV4TPGc/8jHfLVzP59v2o2i1TMhJ3HQcQVKpsmNzeRrhlKvxpCgDryxihAi8kiCLIQQAba1tAHVZWduD5UgzppWAMDyL7aGMqw+bTnk2wg3b3pgWjnHqVYavH2v13j9kw2c+X8raI9O55IsK189djv3n5GCaojh0n+u48kPvgLg5PH5AYkrEKbmJqAYonnhgzVgTmF0cvftp4UQQ5MkyEIIEWDlNgVDex0GffeVNBeeNh3V7WRdUVWII+vdwTobanszI7LTA3K/4YkGvOZkaht73vT32qoN3PVOKSoaHl6Qxp9/eAUA111wGo8uzAeNjq+dmaheD/MLw1/BosO5M30fIp74yLfG+pSxA68bLYSIPJIgCyFEAHm9XuzGJNKN7h7HmKNNGNpqKG6NrC6jdS4dUa7mgN2vcEQaiqLhg3U9z5Q/v2oL6Ay89cNTuersk485d/G8mTx11XjUtgZ01mriYmMCFttgnTlzIqrTRrXBtxxl0WlTwxyRECKQJEEWQogAWr+9CMUYw4Ss3jfh5Zm9OGJSsdkdIYqsd16vF6cxkTRT4Fo5nz3TN+P7xY7iHsccbnKhtNUzeVRet+cXzJ7Eul+fz7s/Py9gcQWCTqcl2l6HotODtZa8jJRwhySECCBJkIUQEe/tz7+mtS3yyqJ1Z8XG3QCcOmFYr+NmjkhF0Rl5d82WEETVty37ilGM0YxKMwfsntPGDEO1t7KzouclFk1qNHFqW6/3yUxJjMgKEfkW3zcASUrv8Qshhh5JkIUQEe22P7/ID9+r4od/fSXcofhl08EaVK+Hs0+a1Ou4RXN85z/auDcUYfXp002+OKYVBC4R1Wg0xDgbqHIauj3f2GLFG5NEfvzQ7EA3e6RvrfbYtAAUjhZCRJTud5AIIUQE+NWS13m3OhZFA9srI6NJRF8ONrnQUEtSXGyv42aMG4Fq28BWW+jLg63esocXP97IjvJm6hwa2rUxqFHxKBot8wsDU8GiQ55Fwy5PCq1t7cTGRB1z7uOvdqJotEzqoV50pLvxglN5/bcvc/vVC8IdihAiwCRBFkJEpL++/CHPFWkwWiswajzUK4H76j+YWjQWUui7VbNGoyHe00QNoZl9/Nt/P+LV9UWUumLAnAIkoKp6DDSRQiuZejunjM1iVF5mQJ87c0Qauw/oeXfNFq4866Rjzq3dcQiI5bRJBQF9ZqhkpyWx9fHbwx2GECIIJEEWQkSc59/7gkc2WNHaG/n4N5fyP0++zZq2ZPYUl0fkWtQOB8qqUGISGRnt8mv82FQT69tTOVBWFbDSat0pq67nz1/bQUnDolZTGKdw9bypzJ85AY0muCvtzpk1nqUHivh064EuCfKO8kZUr5FTp44JagxCCNFfsgZZCBFRVn21g19+VIHibOXtn5xNbnoy8yblA7D8iy1hja0vH6zz1cQ9aXS2X+PnTsgH4PVPNwUrJADeXr0FRaPlR9Nj2P747TzzP4tZMHtS0JNjgFkTClDtVraXdy0fV25V0dnqMBm7X6MshBDhIgmyECKi/Oalz0Cr46WbT2L8CF/zhUWnTUf1eli7pzzM0fXu0x2HATj/lN436HW4ZF4hqtfD6t1lwQyL1btKAP/jCiTfRr36bjfqteniSNY5Qx6TEEL0RRJkIUTEsNkdlHgTSXZWcfLk0Z3HUxIsaK017G/suflGJNhZ60TTUun3conUxDi01hqKgvy69tbYUNsawrY8Jd+iwR2TQnPrN+XQDpRVoUTHMyI5qpcrhRAiPCRBFkJEjH++vgolysJF07q27c0wOLAakvB6vWGIrG82u4M2Uxq5Uf2bEc0yOmgzJuN2e4IUGdR7o4nz9r1xMFhmjEhD0ep5b+03HfU+3rALgOkjMsIVlhBC9EgSZCFExHj1y4OoDht3Xt61bNaUnHgUk5kvNu8JQ2R9e23VBhSDiVNH92+zXWF+Eooxhg/W99yOeTCq6hrxmpMZkRi+db7nzPJ11Pt064HOY1/uKwVgfqFs0BNCRB5JkIUQEaG1rZ0KTQrp3pou9XIBzir0Lbl4d/3OUIfml3c27APgmrNm9eu6S+ZOAeCttcF5Xe+u3YqiaJg9KrDl2/rDt1GvlR1HddTbV21FtVuZNLL7FtNCCBFOkiALISLCY8s+RjHGcOmM7ls0nz17EqrLzsZDtSGOzD87ahworVX9Xud78qRRqLZGNpUGZwnE5zuKATjvpIlBub8/uuuoV23XYXI2hqSShhBC9Jf8ZBJCRIQ3Npag2q3cfun8bs+bjAZM7bWU2rQhjqxvdocTqymVHKOj39dqNBqS1WZqiQvK+uo9VW2otiYmFuQG/N794duol0pzaxterxdnVCLppshcTy6EEJIgCyHCrrHFSo0+jWzqiDYZexyXZ1ZxRqditdlDGF3f3vh0I4ohijmj0gZ0/ZSsWJSYBNZvLwpwZFDrNmFxNwX8vv3l26in4721W/lq1wEUQzRj0ntvxy2EEOEiCbIQIuz+8t8VKIYorjx5ZK/jZhakoegMvLd2S2gC89NbX/o2Dl6zYOaArl940jgAln22OWAxAdQ3t+IxpzAsPvxNU887aQIAn2w9wKpNewGYNTa8s9pCCNETSZCFEGH31tYK1PZmvn/RGb2Ou/Bk3zrajzcFfqZ1MHZUtaO01nQ2Numv80+Ziupo48uD9QGN6701vg56M0eGv5TajHEjOjfqbT5QBcBZMyeEOSohhOieJMhCiLCqqmukwZhBvrYJg773mc4Z40agtjeztTx8NX2/zely02JMJctgG/A9dDotFmctFa7oAEYGn247BHxTZi2cOjbqVTsNHGxwoLbVk52WFO6whBCiW5IgCyHC6pH/foyiN3Lt6eP6HKvRaLC4G6lxR073tTc/24hijObkgtRB3WdCqgk1NpVdBwPXdnpXZStqewvTxnRfGSTUhsVpccekUueNxuJpDXc4QgjRI0mQhTgOrd26lzN/9o+I28z2bV6vl+U761DbGlh83ql+XTMqyYDXnEJZdWCXIwzUW+t3A3D1mYWDus85hb711/9d+VW/r92+v4Rz7vknB8qqjjle4zJgdkVOKbWOjXrEppJtibxqJEII0SEyfmoKIQLG6/Vyw79WsV+by9J3vwh3OL36y8sf4rDkMj/Li07nX8I0Z2wOiqJh+eebghydf7ZV2sBay5TRg5ulvWTeTFSXgy/2Vvb72uv++jZ7lBzOf+gNbHZfqbnm1jbcMankWSLnx/y5s79Z6jEhOzGMkQghRO8i5yenECIgfv7EMuwWX3WAVduLwxtML9xuD4+vLgNrHX/70RV+X/ed06YC8NmOw8EKzW9ut4dmfTKZurZB38scbSKqvZrDNn2/rvv3259RH5OPsaUUuyWXc3/xNADvr9uGotUxY8TASs8FQ8dGPYA5E4eHORohhOiZJMhCHEcOV9byapEHXUs5Sms1e+tcIX2+2+1h18Ey3G5Pn2N/8/SbeCyZXDLahDna5PczhmWlQWsNe+r635Qj0N764msUk5mTRiQH5H6jE7S4zWlU1Db4Nd7t9vDQ+3tR2xpZ/eDVjFZLOWzI4/t/fJ5Ptx4A4OyZfa/tDhWNRoPZ2YDqcTN/hlSwEEJELkmQhTiOfPePr4Axhj9eNoVMfTuthiS/ktVAufjXT3Hekq2MuHc5w27/NxNvf5yzfv6PLhvPbHYHL25rQdNSxe9/cEm/n5Ohs9GsT8bpcgcq9C6q6hq5+rf/pqahuccxy9ftAuDKM6YH5JnzJ+WjaLT856Mv/Rr/8ydexW3J4rLRBlISLLz9wE2YWw7zQa2Fjw/ZUO1WZk/svbZ0qF09M5uJuqp+fSgSQohQkwRZiOPE0nc/p9SYz0i1nIvnzWRaXgKKycwnG3eGLIaiJg9Y6xilqcZCO22qkb1qBuc++glvfvrN5rOfPr4MYlO5cWZKn6XdunP6mDQUk5llK/1LJAfir6+uZK0tjbm/fInWtvYu5+ubW1lT5gRrHTPGFwTkmVcumIXq9bBqR0mfY2samnltvxtdSzkPH/mQYdDr+OA3V6Cx1eO2ZBHtbIiYDXod7rtuIe/8/vvhDkMIIXoVWT85hRADYrM7uP/dfahtDbxwz5UALDzSueytdTtCF4cujjStlRV/vJWtj9/OwSdu4k/nZAAKd75VwgPPvEVji5V3D6vom8v4n2svGNBzbjz/FFTVy7I1uwL7Ao6yt6IRgHZLLqfd8+9jZqtrG1s45X9ewBWbwWVjAldyLjUxDr21iv1Nap9jb/rTyyjRCfzyvDHHbHDMTkvi2Rtmo7a3MDYp/B30hBBiKJIEWYjjwM3/9xJeSwY3TIklPTkBgHmF41HtVjYdbgxJDIcra1Gi4xmedGzCeNn8Wbxz52no7fU8uUfhpHufR4lO4KcLRg54drMgNwN9ayU76oK3fKSs2Yna1sBpsbU0mvM5/Wf/wuv1UlXXyJxfvES7OYvLctr5vzsuD+hzh5k92GPSaW7teePfVzv3s9WZQlJbMdddcFqX83Onj2PTby9g2f03BjQ2IYQ4UUiCLMQQt3XfYVY3xhLbUsyvrl/YeVyn0xLrrKfCFZq1nh9/5VvKMXV416oJEwty+erhq0m2lWC35BLTcpjvX9x7W+m+jIlTccRmBq0ecqNLR5THynP3XcdETRkVpnzO/Pk/OfVXr2CPzeSa4S7+FODkGGDu2CwUnZ7/9rJ85MdPfgjAE98/q8cxSXGxEbe8Qgghhgr56SnEEHfjY++ARss/bpnfJSEanaxHjU2npKou6HF8tde3EW/ulFHdnk+wmPnqr7dyzTA7L989sKUVR1s4aySKRsvT7wSn1rPTYCHJ4AVg+QM3k+c8zEFdHk5zGteP8vLQADYX+uPac09G9XpY/mVRt+e9Xi9l3niSHZXMmhBZG/CEEOJ4IQmyEEPYP15bSV1MPlOMtcyZMqbL+TMm5gPw6gC6s/XXvuoWVKed6WN6rm+r0Wh48PuXMLEgd9DP++45p6A62/l4R/mg7/VtJVV1KFEWchN9y0U0Gg0r//h9Cg2V3D09iv+96TsBf2aH3PRkTK0V7Gnu/sfz219sQomO55QR0mhDCCGCRRJkIYYom93B/60sBmsdz95zdbdjLps/E9Xr4fNdpUGPp9KmoLfX+90Rb7CiTUbiHNWUusx4vd6A3nv9jv0AjMn6JgnV6bQs++1N3Hnl2QF9Vncmp+rwWDLZeaDr+/byZ9sAuOHc2UGPQwghTlSSIAsxRH3/Ty/htaRz0/QEEizmbsekJsahtdawrzF49YI72HQWkvWhbUwyKzcWzMl8tml3QO+7uci3XGT66MHPdA/EladPAmDJ26u7nNtS5UDTUjXo1tZCCCF6JgmyEEPQ9v0lfN5gxtxymF8s7n09b7bJic2UEtSmGofKq7utYBFs3z2zEIAXPv46oPfdV+mr/HFSmJpsfGduIaqtiS/2H9tRr765FVt0BiNinGGJSwghThSSIAsxBN34t7dBo+fxG0/vs1LBjGFJKIZo3l+7JWjxrNzoq0c8dXh60J7RnbnTx4G1jg2lrQG9b1mTr8RbUlxsQO/rL41GQ6bSRL0+Bbvjm2T4329/gaLTc940mT0WQohgkgRZiCGmubWNamMWw9UKX4LYh+/M8X1d//6GPUGLqaOCxelTu69gEUy5hjZajGlYbfaA3bPRpSXKYw3Y/QZi/vgMFGMML320rvPY+1uKUd0Orjt/ThgjE0KI458kyEIMMW99sQlFq2fe+Cy/xp8yeTRqewubS5uDFtPe6lZUp43pY3uuYBEsZ07IQjFE8eIHawN2T4chrrPEW7jcsvA0VK+H19d+88HmULuJGFtVj2vOhRBCBIYkyEIMMSu3HARg0Zwpfo3XaDTEuRuo9kQHLabqdgWDvSEsjSluuvBUX93gDfsCcr+y6npfibeE0DRY6UluejLG1gp2Hyn3tnHXAdTYNKZlhnadtxBCnIgkQRZiiNlRaUVta2TyqDy/rxmXaoLYVPYdrghKTDZ9HMn64FfK6E5mSiLG1gr2tgSmvNza7b4GHWOykwJyv8E4utzbM++vB+DK0yeHOSohhDj+SYIsxBBTp8YQ7+3fcokzp/iWPiz7NLDVHgAOlFWhRMUxPDl4M9R9mZCsxWPJ5FB59aDvtWW/r/HItJE5g77XYF0517d+/Kl31rDuUBNqWyPnnTwlvEEJIcQJQBJkIYaQQ+XVYE5hTEr/vv6/ZN4MVLeLD7cGvmFIRwWLaSGuYHG0s6eOAOA/H28Y9L32VvhKq508OfQbDr9t0WnTUdub+bSonnpdMpma5rAsYxFCiBON/KQVYgh547NNAJw2vn8NLBIsZpId5RSryTS2BLY6w8Z9vhnXuWGoYNHh8jNnonpcfLZr8G2ny5qdqG2NYSvxdjSdTks6jTRE56CYzJw+Ji3cIQkhxAmhzwS5tLSUefPmMW7cOMaPH89f//pXABoaGliwYAEjR45kwYIFNDb6CuurqsqPfvQjCgoKmDRpEps2bQruKxDiBPLF7jJU1ctFp0/v97U3nDYKxRjDg8+9F9CY9la1ojpsTBsTvtq8CRYzRmsVB1sVv69ZvWUP76zu+vOp0anF5AlsXeXBmD82HUWjRVW93HShlHcTQohQ6DNB1ul0/PnPf2bXrl2sX7+exx9/nF27dvHwww8zf/58ioqKmD9/Pg8//DAA77//PkVFRRQVFbFkyRJuvfXWoL8IIU4U++udaKw1ZKYk9vva7190BlhreXtXfUBjqrYrGBzhqWBxtBFx4IxJp7655+TWZnfwv0+9ybjbnuC7Lx/g9jcOsb+k8pgxDoOF5DCXeDvazRfOQfV60LdWMiI7fMtYhBDiRNLnb7SMjAymTZsGQGxsLGPHjqW8vJzly5ezePFiABYvXsybb74JwPLly7n22mtRFIXZs2fT1NREZWVlT7cXQvjJ6/XSok8kTTewhhg6nZaZSW4cllxWfbUjYHG16+NIMYSngsXR5k3IQdHqePmj9d2ev/Hh5xj782U8u19PmzaWAk8Jit7ET5e83TmmorYBJSqOnDCXeDvasKw0phur+d701HCHIoQQJwxdfwYXFxezefNmZs2aRXV1NRkZGQCkp6dTXe3bPV5eXk5Ozje7v7OzsykvL+8cK0Qk8Hq9/Pbfb1HddOx63AtPGs95p0wNU1S9+3LHfpQoC5MSB56M/up7Z3HBki38YdlqzpgxYdAx7S+pRImKY4TZMeh7DdaVZ87i8e3rWLH1ELdfduy5LXsP8XFjAkZXOdeNtXD3lVdgMhqYePvjbNYnUdvYQkqChTXbjpR4ywp/ibejvf67m8MdghBCnFD8/k7UarVyySWX8Je//AWLxXLMOUVRUBT/1/4BLFmyhMLCQgoLC6mtre3XtUIM1osfrOHZ/Xrer0s45n+3/WcbNnv4k73uvLNuOwBnThs54HtMLMglvq2UPfa4gLRmXrlxNwDTRoT/A3BuejLa1mr2Nni6nHtk2WcoioYnrj+FXyy+EJPRAMBd50xAMcVyz7/eAGBzka9l9tSR2aELXAghRMTxawbZ5XJxySWXcM0113DxxRcDkJaWRmVlJRkZGVRWVpKa6vv6Lysri9LSb0pJlZWVkZXVtSXuLbfcwi233AJAYWHhoF+IEP3xzKrtqGTw3FWjiY+NAeC/qzbyUnEy9/zjNR676+owR9jVhv01qGo6F8wZ3Az3NSfl88QO+MML7/G7Wy7265raxhaeefcLVmwtodnxzfrcJpcW4nLCWsHiaLlRTg6Rhs3uINpk7Dy+rtyBlgrOnHn+MeNvuHAuD7/3T1ZZo7A7nOyraAQyOHlSZLweIYQQ4dHnDLKqqtx4442MHTuWu+++u/P4woULWbp0KQBLly5l0aJFncefe+45VFVl/fr1xMXFyfIKEVGcLjcHnHHEt1cwd/o4Jo/KY/KoPB645SJ0LeW8fcAVkbPIh60KhraaYxK/gfjxFWejtjXw2paqXse53R6+98AzjL7tHxQ+tIondsA+NY0ar7nzf05tFOaWw0wZlT+omAJlzuh0FL2J1z/5qvPYum37cFmyKUzt/sfd4lmZYE7mN08vp7TZgWprJCXB0u1YIYQQJ4Y+Z5DXrFnD888/z8SJE5kyZQoADz30EPfeey+XX345Tz/9NHl5ebzyyisAnHfeebz33nsUFBQQHR3NM888E9QXIER/PfP25yjR8Zyff+xGLI1Gww2zMliyW8N9/3qDR++8MuixeL1e5v7kH+QnRfP8L6/vcZzd4cQRncoIpfek1h8GvY4plna2uHNYt20fJ/UwW/rAs2/zhTUVDZWM1VRx3tThLD7vdOKOzLhHoqvmz+CFg9t476t9fPdcX0m0v73xBZDOnRd1XyLtnu+ez1N3LuXVHV50qhaTEjkl3oQQQoRHnwnynDlzUFW123MrV67sckxRFB5//PHBRyZEkLzwxW5UTTp3XbGgy7l7v3c+T9/xNG9Y9fze4excqxosj/7nQ0qN+ZS0eHj9kw1cPG9mt+M+WLcVRW+kMCclIM/95dVncOlzu3no5U94u4cE+Z2t5aiaRHY9em3Q/xwCZfyIHLCuZEdre+exDdVedJRz8uTzu71Gp9Ny4cgo3qqy4PB6yHIGvtugEEKIoUU66YkTit3hpMSbSLKzqtuv0TUaDYsLU8Gcwi+ffDOosbjdHh5fUw7WOnC0cc+rm3G7u24wA/hw414Azp89PiDPnjG+gNjWUra1xmB3OLucb25to1afRrbSMGSS4w4Z2jaa9cm43R4+2bgTjyWT2Zn6Xq956JaLUNsaUTRacuIjp8SbEEKI8JAEWZxQ/vH6KpQoCwundN042uEX116ApqWS13a34XQFr77vfUtex2vJ4PKx0VxaoMFlyeaOR//T7ditZc2odiunTh0TsOdfNj0TJSaBR//7UZdzj7++CsUQxcUzhgfseaEyY1giisnMB+u38ve31gJw96Vze73GHG1iTqoLgDFZ/W/CIoQQ4vgiCbI4obyy/gCq08aPL++6vKKDTqfle1OTUGNT+dWTbwQlDqvNzn932dC0VPLQ9y/m/26/DFNLCe9XGNh1sKzL+CqnEbOzPqDd6n52zbmo7c3858vDXc4t/7oE1dHG9y+aF7Dnhcplc6cA8NbanWyu02BoLmXamL4T/b/feRknRVfzo8vPDHKEQgghIp0kyOKEYbXZqdCkkOau6XOj2a+uX4impYpXdrb2uOxhMO567BUwJ3PryZnodFo0Gg1/v24OaA0sfnT5MWNrGprxmFMpSOhXX58+RZuMjItqoTkmh637vkmSrTY7VdoU0j21mKOH3nKDUyaPRrU1sbLYjteSzql50X5dl2Ax859f30BSXGyQIxRCCBHpJEEWJ4zHln2MYozhkhn5fY7V6bRcNNaMGpvGc++vDmgcNQ3NfFShw9hSyk+uPqfz+JkzJzLZUE1tTD5/fflDXvl4PZf++ilO+eXLKBotJ4/peVnIQN172WkoGi2/e2FF57F/vfEJijGGRdNzA/68UNBoNCR5m/BYMlG9Hn5y+RnhDkkIIcQQIwmyOGG8sfEwqt3K7ZfM92v8T65cgOp28uJnuwIax21/eRUlKo5fnD+uy5KJpfdeA9ZaHt3i5ucf17PRmYEXDZM0Zdx2ceATvbnTx2FqKWFjg75zpvz1rw6iOtu5PQjPC5Up2WYAoq3ljBsuXfGEEEL0jyTIYkjyer19DzpKY4uVal0aWdT5vWwgMyURS3sF+x3mfj+vJ8s/28hX1gQsrcUsPv+0LucTLGZ+d94IsuzFXJrdxnu3TObAEzfz1kPfJzYmKiAxfNuiCclgTubxZR9jdzgpI4kUV3VE1zvuy+WnTQZgwaj48AYihBBiSArsokYhQuD1TzZw19ulPHBmKt8779Q+xztdbi757XMohjyumFLQr2ctGJ3E6xWxvPzROq4+55SBhgzAnuJy7ly2CxQNL951YY/jvnfeqX69rkD5xffO4+VfvcPS1XXotBoUUyznjxhct75wO+fkKTyp0TB/5oRwhyKEEGIIkhlkMeS89Mk2FIOJ37+zvc+xhytrmXLnEg7q8shzHub2S/tXoeDOy+ajetw890nfz+qN1WZn0R/eRjWa+cMFI5hYEDnre+NiYyjQNVAflcVTn+1DddmPi0oOC2ZPCmjVDyGEECcO+e0hhpzddb7GFjZLHs+/90WP495bs5m5D75LW0wm56c08dkjt6HTafv1rLyMFMxt5expixrwMguv18vZv3gaR1wOVw73cOVZJw3oPsH0s4tPQtHqaTTnk+iokkoOQgghTmiSIIshxe5wYjWlkmYrRm1v4Q89zCL/3wvvcetrB1C1Bu6fm8jjP7lmwM88fUQcmJN587ONA7r+xj88T7kpn/FKGX+47bIBxxFM55w8BUOzr8XyOePTwhyNEEIIEV6yBln4rb65lT+99CEuzzczqUa9lp9edTYJFnNIYnjri00ohihOL0iluKaFL+15vPTBmmPWB7/9+df8fXM7Onsjr/5ovl9NInpz56XzeOfxjfz7o81cPG+m39c5XW6uf/h5VrcmEddWzPK//mBQcQTb4lmZPL2hijsvuzjcoQghhBBhJQmy8NsPHvkvXzkyuhz/8L6lbHzs9pDE8M6Xe4AUrjhjOpnJCcx64CN+/3ZxZ4J8qLyaH76yAzRa3vrJOYwfkTPoZ47KyyTaWs5OxYTX6/VrXetnX+/i5qc+xxmXQ1xbMR/df3W/l3eE2n3XLeS+68IdhRBCCBF+ssRC+O3rWgV9SxlLLxvW+b+xlFJ3pLFFKGyrbANrHdPGDCc9OYEZllZaY/N55eP1OF1uLnjwNdSoOB44Jy8gyXGHOXkxqLGpfLBua6/j3G4P1/9+Kde+tAeHKZFLsqxseexW0pMTAhaLEEIIIYJLEmThl/fWbPa17c01MXf6uM7/vXzf96C1hkdXV9Lc2hbUGLxeL43aRNI01s5jf7n9IlRHGw+88TUX/fpp2ix5nJ/eFvAyaT+65HRU1cuTH/S+Dvn8+57kk+ZkYturWP6DGfz5h1dIJQUhhBBiiJHf3MIv/3x3g69t72XHdleLi43h7tOywJzCtQ+/GNQYVm7YgRJloTAvvvNYdloSU2OaaInNZ6eaTbajmCcGsSGvJxMLcjG2lLO1XulxjNVmZ48jgQRrMdv+/gOmjB4W8DiEEEIIEXySIIs+eb1etjcbiLaWd7ts4UdXnEVKWzFbHKl8vGFw9YJ788Ya370vOnXSMcf/dsfFqHYr+pYy3n/ghqA9f3a2Ca8lg1Vf7ej2/F/++xGKyczVs4fJrLEQQggxhMlvcdGnNz7diBqbyrwRlh7HLL1rEXic/PDZ1QFry/xtGw83obY3c0bh+GOO56Yn894PT2bT/10btHbMAD+5bB6qx80D//282/OvfV2K2t7CDy8b+k02hBBCiBOZJMiiT099tAnV4+YnV8zvccy44dmck+Gg3ZLLPU8sC0ocNV4ziZ7Gbmdnx4/ICWpyDDB5VB7pzjIOqGkcKKs65lxJVR0NpkxG6BsxGQ1BjUMIIYQQwSUJsuiV1+tltzWK2LZyRmSn9zr28buvRtdSzqt7bAGPY+OuA2BOZlJmTMDv3R+/ufxkFL2Jn/7r7WOO//Glj1B0Bm5eMCU8gQkhhBAiYCRBFr166cO1YE5mwejEPsfqdFpOyzWCOYVPNu4MaByvfLIJgAtmjQ3offvrvFOmEttazKYWM/XNrZ3HP97fjNJawxULZocxOiGEEEIEgiTIolfPrtqO6nbxkysX+DX+hnNmAbD0o4G1Ze7Juv01qE4bi06bHtD7DsSdZ41DibLw83+8Afhmt9tjs5ma6JbNeUIIIcRxQH6bix653R6K7LHEtZeTnZbk1zVzpoxBaa3hq7LA1kQud5gw22sx6MPf/PGGC+eiby5jZZmK3eHkz69+iqJouOviOeEOTQghhBABIAmy6NGz736OEpPAueNT+3XdsKh2rNEZxyxBGIwvNu/Ga0lnXEpkbH7TaDR8b0Y6mJO5/99v8WW1ir65jFOnhnf5hxBCCCECI/zTcce5tVv38q931x1zLDPBzP03fSciZkN78/xnu1GVdH5y5Vn9uu7C6cP561YPT7/9OT//7vkDfr7X6+XOv77MWyV6UDTctGjGgO8VaL+49gKeufM5XtqlR7FkMD+hPtwhCSGEECJAZAY5iFrb2rn6yfV81pJyzP/+cziKW//8UrjD65XX66XYFUu8vZLUxLh+XXvDBaeiuuy8u+nwgJ+/dd9hJt3xT96ujsPkqOPFa8dz9kmTB3y/QNPptJw/wogSk4DqcXPPVf6t0RZCCCFE5IvsKcwh7rZHXwZzKjeN9vC9c07qPH7u717lY0cKW/cdZvKovDBG2LNlqzagxCRyRoa+39fGxcZgsVdxWDHj9Xr7tXHN6/Xy8yeW8eoBICqdeZY6nnrgFnQ6bb/jCLbff/8i3r53GfHeFgpyF4U7HCGEEEIEiMwgB8nhylo+r4smuuUwv7x+IXkZKZ3/e/yGuaDVccNj74Q7zB69sGorqtfD7RfNHdD1s3NjwZzMqn6Ue9t1sIypP/wHy8piMNgbeebKUTz7i8URmRwDxMZE8f5d83j/f68KdyhCCCGECCBJkIPk5keXgTGa318xs8u5eYXjmRZVR31MPo+89H5I4qlpaOad1Zv8Hr+jSYvJWkFBbsaAnnf9kXJvz6342q/xv1zyOuf+bTVNUZmcHF3Nzr/dyBkzJgzo2aE0bng2mSl914gWQgghxNAhCXIQrNu2j73eDDLsJSyaW9jtmOfu/S5Kaw1/W1tNY4s16DGd/5sXuOOdSi761ZO43Z5ex67duhevJYMZmcYBP+/kyaNRWqvZWN53V73T7n6CFw4a0Ttb+NfFw3jp1zdE/AZGIYQQQhy/JEEOgh8+uQJUL0/cem6PY2Jjorh7bhaYU1j88ItBjWfjrgPUROWgtjWw2ZVJ4Y//SU1Dc4/j//mOr+rG988fXFe4EdEO2qIzqW1s6XHM/pJKDutzyGgvZsej13HOyVMG9UwhhBBCiMGSBDnAXv5oHXUx+Uw01DJtzPBex/7w8rNIaStmqyuNFeu3BS2m+5au8MV28yxONdfQGJ3D7F8uY/WWPd2O31DejqalatB1fRcWjkDR6Xnq7c97HPP0u2tQFA0/PHcKJmNk1DkWQgghxIlNEuQA8nq9/O+bW1DbW1hy92V+XbP0rkXgdnHvi18EJaaqukb2OBNJspVy0qRRPP/L6/nJdBMeg5lrnt3Km59+dcz4Q+XVtJuzGBvnHvSzr7/gVFSnnfe3lPQ45pM91ah2K5fOnzXo5wkhhBBCBIIkyAH06H8+xG7J5cwMl98bt8YNz6ZAW0edKZvt+3tOJAfq3iVvoRhjuPv8b2oI/+iKs3jxusngdXHXq9uP6Xj399c/RdFouWbu4DfIxcZEEeeoosQZg9fr7XLe6/VSqcaR5K6TNcdCCCGEiBiSIPfA6/Uy5rZ/kH/Hs1x1/9PsO1zR63i328MTa8rAWsdff3R5v571yytPQ9Hq+PXSDwcTchdOl5tPK8DQXMp3z51zzLk5U8bw0zmpeM0pfOd/n+s8vmpvHaqticvPHNz64w5zCxLAnMyLH6zpcu69tVtQohM4ZURCQJ4lhBBCCBEIkiD34K///Qi7JRdUlXXt6Sx4bAPTf/g4S9/tfj3tr596E48lk8vGRGGONvXrWfMKxxPTcphNzdHY7I5AhA/Ag8++DeZkrp6e3u35H15+FmMpp9SYz/1Pv0lzaxsNhnRytU0Bqz183/fORXXa+cdHXddY/+eTrQBcf25gknEhhBBCiECQBLkH//yiGLWtgR3/dxVPXJjJCCqo06Xw68+b+c2Tbxwz1mZ38NL2FjQtlfz+B5cM6HnXnpSHEh3Pg0sD1zzkP5uqwVrHLxZf0OOYZb+5Dl1LOf/e7uB/lryJYjCxcPqwgMWQnpxAlreKck06VXWNx5zbXNmO0lrV52ZGIYQQQohQkgS5G0+/9SkOSw6nZ3gwR5s475SprPrTbXx53wIMLRU8uxeWvLmqc/xP/v4qxKZy8+z0Ac+83nXl2WCtY9mW2oC8huff+wJnXA6nZ9Lr+l5ztIkl158MWh3vVseiOtv5wUXzAhJDh+8vmIhiMPG7pe91HmtssdIWncGIaGdAnyWEEEIIMViSIHfj0Q92oba38Mjtx84Gpycn8MF9i9C0N/HgZ3W8s3oTjS1W3ivxrfO957vnDfiZBr2OU9K8OOJyulSW6C+7w8n97+xGtbfyxx98p8/xZ8yYwIVZDhSNlkRnNbExUYN6/rd979w5KK1VfHTgm4Yoz7zzBYrOwLlT8gP6LCGEEEKIwZIE+VteW7UBqyWPmfFtJMXFdjk/Ijudl2+dAx4Xt/93F5f+9nmU6AR+fs5oNJrB/XE+cMN5qC4H//fmhkHd53sPPYfbksV3x+pJTYzz65q//fhK5sfX87srTxrUs7uj0WiYk6HBZcnm7c99raff31yM6nZy3QVz+rhaCCGEECK0JEH+lgde/wrVYePPt36nxzGzJozkb5eMBp2RA7pczC2HuWnR4JclDMtKI9tTSZk2g8OVA1tq8fbnX7PBlkyitZgHv+//emiNRsPT917LBXOmDei5ffnNteegelw8+taXABxoMxBtq+z2Q4gQQgghRDhJgnyUFeu30RCTywRTA7npyb2OXTS3kHvnJKJtqeD3VwauycVPFs1A0Ru57+n+b9az2R38+OVN4LTxys8vDlhMgVCQm0FSezkH3El8sXk3Xks6U9P7V+1DCCGEECIUJEE+orm1jbuf+wLcLh79wYV+XXPrJfM58MTNXHja9IDFcfG8mRibS/miWofVZu92zJ7icibc9gQX/+pJtu473Hn86geW4rFkcsOkaApyMwIWU6AsnlOAYjLz/Sc/AeCq0yf3cYUQQgghROhJggxs3XeYwnteoNWSz9zEFkblZYY1nlvm5KHEJPLzf7zW7fnb/r6c1tgcNrkyWfjUVibf/ji3/ukFNjvSSGkr5jc3fie0Afvp9kvPhLZ6bJY8VFsT58+ZGu6QhBBCCCG6OOET5KXvfs7Cv6/GaUrmugIXz913XbhD4q6rzkbXUs57h9xdGods2nOQA2SQaS9h6WXDGKtU0KRL5P26BHBYeeXeS8MUdd90Oi3T4n2vJ11pGvSmRiGEEEKIYDihM5QfPvoSv/6kAcXr5u/fyed/b/pOuEMCfBvmbj4pC8zJ3POtWeQ7//U+KAqP3LiAudPH8cEffsCOh77D4hFO/nbJaIZlpYUpav/88pr5qPZWLp0RuGYkQgghhBCB1HMHiePc259/zdvVcUTbDvPefZdEXGL5s2vO5ck7nuJtq5E/2B1Em4ys27aPEl0Wue5yTpr0nc6xsTFR3H/zReELth+mjRnO4b9I5zwhhBBCRK4Tdgb5wtOmc+dkLZsfvSnikmPwzSJfPzMdzMnc9y9fa+u7n14Bqspfv39umKMTQgghhDh+nbAJMsBdV52DyWgIdxg9+p9rL0DbUsEbRXZWrN9GhTGHEVQybYzMwAohhBBCBMsJnSBHOo1Gw+LCVDCncPOLW8Dj5rFb/StBJ4QQQgghBqbPBPmGG24gNTWVCRMmdB5raGhgwYIFjBw5kgULFtDY2AiAqqr86Ec/oqCggEmTJrFp06bgRX6C+MW1F6BpqYSYJMboahg/IifcIQkhhBBCHNf6TJCvu+46Pvjgg2OOPfzww8yfP5+ioiLmz5/Pww8/DMD7779PUVERRUVFLFmyhFtvvTU4UZ9AdDotPzw1G21LBY/fvijc4QghhBBCHPf6TJBPO+00EhMTjzm2fPlyFi9eDMDixYt58803O49fe+21KIrC7NmzaWpqorKyMvBRn2DuuuocDjxxc0R2xxNCCCGEON4MaA1ydXU1GRm+ZC09PZ3q6moAysvLycn5ZglAdnY25eXlAQhTCCGEEEKI0Bh0HWRFUVAUpd/XLVmyhCVLlgBQW1s72DCEEEIIIYQIiAHNIKelpXUunaisrCQ1NRWArKwsSktLO8eVlZWRlZXV7T1uueUWNm7cyMaNG0lJSRlIGEIIIYQQQgTcgBLkhQsXsnTpUgCWLl3KokWLOo8/99xzqKrK+vXriYuL61yKIYQQQgghxFDQ5xKLq666ik8//ZS6ujqys7O5//77uffee7n88st5+umnycvL45VXXgHgvPPO47333qOgoIDo6GieeeaZoL8AIYQQQgghAklRVVUNdxCFhYVs3Lgx3GEIIYQQQogTSE85qHTSE0IIIYQQ4iiSIAshhBBCCHEUSZCFEEIIIYQ4iiTIQgghhBBCHEUSZCGEEEIIIY4iCbIQQgghhBBHkQRZCCGEEEKIo0REHeTk5GTy8/PDHcaQV1tbK227I5C8L5FL3pvIJe9NZJL3JXLJezMwxcXF1NXVdTkeEQmyCAxpuBKZ5H2JXPLeRC55byKTvC+RS96bwJIlFkIIIYQQQhxFEmQhhBBCCCGOIgnyceSWW24JdwiiG/K+RC55byKXvDeRSd6XyCXvTWDJGmQhhBBCCCGOIjPIQgghhBBCHEUSZCGEEEIIIY4iCbIQQgghhBBHkQR5CJJl45FL3pvI5PV6wx2C6Ibb7Q53CEIMOfJ7JjQkQR4idu7cyaeffgqAoijhDUYcY/fu3axbtw6Q9yaSbN++nT//+c8AaDTyoy6SrFu3jptvvpmvvvoq3KGIb9myZQtPPvkkVVVV4Q5FHEVygNCTKhYRzuv1cscdd7Bq1Spyc3OZNWsWixYtorCwEK/XK7/4w6i5uZmf/vSnbNiwgZSUFGbNmsX1119PQUFBuEMTwMKFC/nwww/58MMPOf300/F4PGi12nCHdcJ78skn+dvf/sZtt93G9ddfj16vl/clArhcLu644w42btzI2LFjMRqN3HLLLcyaNSvcoZ3QJAcIH/mTjXCNjY1YrVb27NnDiy++SFJSEn/+85+xWq3yDyPM/vjHP6KqKlu3buVf//oX9fX1FBcXhzusE17H1/annXYad955J7/85S8B0Gq1stQiApSUlPDggw9y6623YjKZJDmOENu3b6e5uZmvv/6aF154Aa/XS3JycrjDOuFJDhA+8qcbgVasWMGKFSsAaGlpYe3atbS1tZGSksIll1xCQkICf//73wFZixRqK1as4KOPPgLg1ltv5be//S0AI0aMoKmpie3bt4czvBPWihUr+PjjjwHQ6XSoqsqHH37IzTffTGpqKk899RTgW2oh/2ZC6+j3prm5mZ07dzJz5kxWrVrF2WefzUMPPcTrr78OyM+zUDv6d41Wq+WVV16hubmZ119/nfXr17Ny5Uo2b94MyHsTSsuWLeOJJ54AJAcIJ0mQI8jOnTu58soreeihh0hISABg2LBhnHLKKfzlL38BICMjg0suuYQtW7ZQWVkpa5FCpOO9efDBB0lMTAQgOzubzMzMzhnLqKgoRowYEc4wTzhH/5uJj48HfDPIiqIwZcoUcnJy+OUvf8n//d//cdlll1FWVib/ZkKku/cmLi6OuLg4vve97/Hmm29y++23k5GRwW9/+1u2bt0q702IdPe7ZvLkydx7773cdttt/OAHP+AXv/gFpaWl/PrXv2bfvn3y3oSA1Wrlkksu4U9/+hMJCQm43W7JAcJIEuQw6/j019DQwGmnnUZiYiKffPIJhYWFnWOuu+461qxZw6FDh9DpdKSlpWEymbDZbOEK+4TQ3Xvz6aefHvPeHK28vJycnBxAqiYEU1//ZnQ6HTabjaqqKoqLi3nxxReprq6mpqaG7OxsPB5POMM/rvX23nT8m7j//vvZsmULGRkZLFy4kOuvv57zzjuP5cuXhzP0454/782DDz7ImDFjWLZsGd/73vf48Y9/zLBhw1izZk04Qz+uHT0DXFpaSlpaGuvXr+eqq67qXH7UkQMcPHhQcoAQkgQ5zOx2OwCJiYn87Gc/w+FwAPDss8/y4YcfcvjwYebNm8e0adP42c9+BsCECRM4fPgwRqMxbHGfCHp7bz766CMOHjwI+BKy/fv3k5iYyNSpU/nHP/7B7373O5qamsIV+nGtr/dl//79REdHo6oqM2bMwGq1smrVKkpKSti2bZuseQ2i3t6bjz/+mP3795Obm8t1113Ha6+91nldTU0NJ598clhiPlH09d4UFRWhKAo6nY5XXnkFgKSkJMrLyxk3blzY4j7edbwvANu2baOsrAyAJ554gvvvv5/Vq1czfvx4TjnlFH76058CkgOEilSxCJMVK1bwxz/+kdGjRzNnzhyuvPJK2tvbmTVrFrW1tZx00knk5OTwySef8MYbb5CTk8PcuXMpLCzs/NT/2GOPYTab5SuWAPPnvcnNzWXVqlW89tprjBw5khUrVvCDH/yA3NxcTCYTf/nLXxg9enS4X8pxxd9/M5999hkvv/wyBw4cYOTIkYwaNQqA559/nrlz55KbmxvmV3L88fffzMcff8w777xDfn4+l1xyCSNHjuTTTz8lMzOTxx9/nIyMjHC/lOOOv+/NypUreeONN3A4HCxcuJCLL76Y9evXk5WVxWOPPUZKSkq4X8pxpeN9GTNmDCeffDJXXXUVRUVF/OlPf8LlcuFyuZg+fToffvghF110Ed/97nc5++yzmTx5cuc3mZIDBJkqQq6oqEidOXOm+uabb6qbNm1Sr776avXBBx9UVVVV33rrLfXZZ5/tHHv99derP//5z1VVVdWqqip1zZo16vLly8MS94mgP+/NDTfcoN57772qqqrq888/ryYkJKgrVqwIS9zHu/68L9ddd536q1/9qvO/PR6P6vF4Qh7ziaK//2Y6fp41Nzeru3fvVj/88MOwxH0i6O+/m/vuu09VVVXdtGmT+s9//lN9/fXXwxL38a679+VPf/qT6nK51LvvvludPn266nQ6VVVV1eeee069+eabVVVV1erqaskBQkgS5BA5+pf0Cy+8oN56662d555++mk1Li5Ora6uPma8qqrqsmXL1B/84AehDfYEE4j3RhKwwBvM+3L0WBF48t5ELvldE5l6e1+eeuopNS4uTm1sbFQ/++wzdd68eeoLL7ygqqqqbt26VV20aJH8jgkDWYMcAs888wzZ2dn86lf/3979hDT9x3Ecf7F+MRisRZ28FCGDwMTqZkYiRC5Ju6wQPHTrYIc6zBLkd+hmFBEEMogglUgQpC6CeAwGLUQbgx1ibHko+gfBzNCNvX+HcHw9/fCXv30/c8/HbcrgPZ4M327fffa3JKm9vV0zMzMqFAqSfh/Q3traWru+SPp9HNXk5KTu3r2rWCzmy9zN4E/b9Pb21n6G3fOnXS5cuODL3M1gt54z2H38rXHTv3XZOq3i9u3btfPbHz58qHv37mlwcFBnz56VxJFudef3hr7XlUolu3z5sj169MhOnTpluVzOzMxu3rxpg4ODdubMGRsaGrJMJmN9fX32+fNn+/btmyUSCevu7rZ0Ou3zI9i7aOMmuriLNu6ijZt20uXixYv26dMnMzNLp9OWTCYtlUr5OX5TY0Gugw8fPpiZ2Z07d+zq1atmZlapVOz79+/2+vVrMzNbXV21a9euWblctnK5bMVi0bd5mwlt3EQXd9HGXbRx0066/Pr1y7c5sR3vC9fB1qfmb926pUKhoIWFBe3bt0+RSKT21kkymVQoFJL0+9iwo0eP+jZvM6GNm+jiLtq4izZu2kmX/fv3+zkqvPze0JtNMpm0c+fO1W6/efPGBgYGtr21An/Qxk10cRdt3EUbN9GlcXAOch1Vq1UFAgHF43G1tLQoGAzq/PnzikajfEWxz2jjJrq4izbuoo2b6NJYuMSijgKBgNbX1/Xlyxe9ePFCR44cUSwW44nhANq4iS7uoo27aOMmujSWv/weoNlMTEzo9OnTWlxc5GsiHUMbN9HFXbRxF23cRJfGwSUWdbb1FgvcQxs30cVdtHEXbdxEl8bBggwAAAB48G8MAAAA4MGCDAAAAHiwIAMAAAAeLMgA4JgfP35oYmJCkvTx40fF43GfJwKA5sKH9ADAMcViUZcuXVI2m/V7FABoSpyDDACOGR0dVT6f18mTJxWNRpXL5ZTNZvXs2TO9fPlSP3/+1Pv375VIJLS5uanp6WkFg0HNz8/r0KFDyufzunHjhr5+/apQKKQnT57o+PHjfj8sAGgYXGIBAI4ZHx9Xa2urVlZWdP/+/W2/y2azmpub09u3bzU2NqZQKKTl5WV1dnZqampKknT9+nU9fvxYS0tLevDggYaHh/14GADQsHgFGQAaSE9Pj8LhsMLhsCKRiPr7+yVJ7e3tymQyWltbUyqV0pUrV2r32djY8GtcAGhILMgA0EC8X08bCARqtwOBgCqViqrVqg4ePKiVlRWfJgSAxsclFgDgmHA4rFKp9J/ue+DAAR07dkyzs7OSJDPTu3fvdnM8ANjzWJABwDGHDx9WV1eXTpw4oZGRkR3f//nz53r69Kk6OjrU1tamV69e/Q9TAsDexTFvAAAAgAevIAMAAAAeLMgAAACABwsyAAAA4MGCDAAAAHiwIAMAAAAeLMgAAACABwsyAAAA4MGCDAAAAHj8AxjcffUaAb2lAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# Must pass the name of the value columns to plot\n",
    "air_passengers_ts.plot(cols=['value'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can plot multiple time series from multi_ts by passing in the name of each value column we want to plot\n",
    "multi_ts.plot(cols=['v1','v2'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Utility functions\n",
    "\n",
    "Here we provide examples of a few useful Kats utility functions for `TimeSeriesData`.  They can be helpful for working with external libraries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert to `pandas.DataFrame`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949-01-01</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-02-01</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-03-01</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-04-01</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949-05-01</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time  value\n",
       "0 1949-01-01    112\n",
       "1 1949-02-01    118\n",
       "2 1949-03-01    132\n",
       "3 1949-04-01    129\n",
       "4 1949-05-01    121"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_passengers_ts.to_dataframe().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert to `numpy.ndarray`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[Timestamp('1949-01-01 00:00:00'), 112],\n",
       "       [Timestamp('1949-02-01 00:00:00'), 118],\n",
       "       [Timestamp('1949-03-01 00:00:00'), 132],\n",
       "       [Timestamp('1949-04-01 00:00:00'), 129],\n",
       "       [Timestamp('1949-05-01 00:00:00'), 121]], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_passengers_ts.to_array()[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check basic characteristics of the time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_passengers_ts.is_empty()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_passengers_ts.is_univariate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_ts.is_univariate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Forecasting with Kats\n",
    "\n",
    "We currently support the following 10 base forecasting models: \n",
    "\n",
    "1. Linear  \n",
    "2. Quadratic   \n",
    "3. ARIMA   \n",
    "4. SARIMA   \n",
    "5. Holt-Winters   \n",
    "6. Prophet   \n",
    "7. AR-Net   \n",
    "8. LSTM   \n",
    "9. Theta   \n",
    "10. VAR   \n",
    "\n",
    "\n",
    "Each models follows the `sklearn` model API pattern:  we create an instance of the model class and then call its `fit` and `predict` methods.  In this section, we provide examples for the Prophet and Theta models.  A more in-depth introduction to forecasting in Kats is provided in the Kats 201 tutorial.\n",
    "\n",
    "\n",
    "\n",
    "## 2.1 An example with Prophet model\n",
    "\n",
    "We will demonstrate how to use Prophet model to forecast with the `air_passengers` data set.      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    }
   ],
   "source": [
    "# import the param and model classes for Prophet model\n",
    "from kats.models.prophet import ProphetModel, ProphetParams\n",
    "\n",
    "# create a model param instance\n",
    "params = ProphetParams(seasonality_mode='multiplicative') # additive mode gives worse results\n",
    "\n",
    "# create a prophet model instance\n",
    "m = ProphetModel(air_passengers_ts, params)\n",
    "\n",
    "# fit model simply by calling m.fit()\n",
    "m.fit()\n",
    "\n",
    "# make prediction for next 30 month\n",
    "fcst = m.predict(steps=30, freq=\"MS\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>fcst</th>\n",
       "      <th>fcst_lower</th>\n",
       "      <th>fcst_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1961-01-01</td>\n",
       "      <td>451.152463</td>\n",
       "      <td>437.745859</td>\n",
       "      <td>463.725516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1961-02-01</td>\n",
       "      <td>432.553525</td>\n",
       "      <td>419.334275</td>\n",
       "      <td>446.021868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1961-03-01</td>\n",
       "      <td>491.921076</td>\n",
       "      <td>479.411625</td>\n",
       "      <td>505.541153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1961-04-01</td>\n",
       "      <td>494.501049</td>\n",
       "      <td>481.066679</td>\n",
       "      <td>506.866898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1961-05-01</td>\n",
       "      <td>503.407884</td>\n",
       "      <td>489.616137</td>\n",
       "      <td>515.606233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time        fcst  fcst_lower  fcst_upper\n",
       "0 1961-01-01  451.152463  437.745859  463.725516\n",
       "1 1961-02-01  432.553525  419.334275  446.021868\n",
       "2 1961-03-01  491.921076  479.411625  505.541153\n",
       "3 1961-04-01  494.501049  481.066679  506.866898\n",
       "4 1961-05-01  503.407884  489.616137  515.606233"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the predict method returns a dataframe as follows\n",
    "fcst.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the results with uncertainty intervals\n",
    "m.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 An example with Theta model\n",
    "\n",
    "\n",
    "We will now use the Theta model to forecast with the `air_passengers` data set.  \n",
    "\n",
    "The Theta Method (Assimakopoulos and Nikolopoulos, 2000) is a univariate forecasting method that fits two Theta lines: 1) a linear interpolation (called the `Theta=0`) and 2) a second-order difference (called the `Theta=2` line), and then combines them to build a forecast.  Prior to running this forecast, we test the time series for seasonality, deseaonalize if seasonality is detected, and then reseasonalize the calculated forecasts.  \n",
    "\n",
    "Hyndman and Billah (2003) showed that the Theta Method is equivalent to simple exponential smoothing with drift.  In Kats we use this underlying model to calculate prediction intervals for `ThetaModel`.\n",
    "\n",
    "Our implementation of `ThetaModel` in Kats is similar to the [thetaf function in R](https://pkg.robjhyndman.com/forecast/reference/thetaf.html).  Because each of our time series models follow the `sklearn` model API pattern, the code using `ThetaModel` is quite similar to the example above using \n",
    "`ProphetModel`: we initialize the model with its parameters and then call the `fit` and `predict` methods.  We can then use the `plot` method to visualize our forecast."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import param and model from `kats.models.theta`\n",
    "from kats.models.theta import ThetaModel, ThetaParams\n",
    "\n",
    "# create ThetaParam with specifying seasonality param value\n",
    "params = ThetaParams(m=12)\n",
    "\n",
    "# create ThetaModel with given data and parameter class\n",
    "m = ThetaModel(data=air_passengers_ts, params=params)\n",
    "\n",
    "# call fit method to fit model\n",
    "m.fit()\n",
    "\n",
    "# call predict method to predict the next 30 steps\n",
    "res = m.predict(steps=30, alpha=0.2)\n",
    "\n",
    "# visualize the results\n",
    "m.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Detection with Kats\n",
    "\n",
    "Kats provides a set of models and algorithms to detect outliers, change points, and trend changes in time series data.\n",
    "\n",
    "\n",
    "## 3.1 What are the algorithms?\n",
    "\n",
    "To detect a specific pattern, we provided different algorithms, which is summarized as follows.\n",
    "- **Outlier Detection**. This usually refers to a abnormal spike in a time series data, which can be detected with `OutlierDetector`\n",
    "- **Change Point Detection**. This refers to a sudden change that the time series have different statistical properties before and after the change. We provided three major algorithms to detect such patterns:\n",
    "    - CUSUM Detection\n",
    "    - Bayesian Online Change Point Detection (BOCPD)\n",
    "    - Stat Sig Detection\n",
    "- **Trend Change Detection**. This refers to a slow trend change on the time series data, which can be detected with Mann-Kendall detection algorithm, `MKDetector`\n",
    "\n",
    "In this tutorial, we will demonstrate the usage of two detectors: `OutlierDetector` and `CUSUM`.  A more in-depth introduction to detection in Kats is provided in the Kats 202 tutorial.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 An example with outlier detection method\n",
    "\n",
    "We provide the `OutlierDetector` module to detect outliers in time series.  Since outliers can cause so many problems in downstream processing, it is important to be able to detect them.  `OutlierDetector` also provides functionality to handle or remove outliers once they are found.\n",
    "\n",
    "Our outlier detection algorithm works as follows:\n",
    "\n",
    "- We do a [seasonal decomposition](https://www.statsmodels.org/stable/generated/statsmodels.tsa.seasonal.seasonal_decompose.html) of the input time series with additive or multiplicative decomposition as specified (default is additive)\n",
    "- We generate a residual time series by either removing only trend or both trend and seasonality if the seasonality is strong.\n",
    "- We detect points in the residual which are outside 3 times the inter quartile range.  This multiplier can be tuned using the `iqr_mult` parameter in `OutlierDetector`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our example below copies the `air_passengers` data set and manually inserts outliers into it. We then use `OutlierDetector` to find them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# deep copy the air_passenger_df \n",
    "air_passengers_outlier_df = air_passengers_df.copy(deep=True)\n",
    "\n",
    "# manually add outlier on the date of '1950-12-01'\n",
    "air_passengers_outlier_df.loc[air_passengers_outlier_df.time == '1950-12-01','value']*=5\n",
    "# manually add outlier on the date of '1959-12-01'\n",
    "air_passengers_outlier_df.loc[air_passengers_outlier_df.time == '1959-12-01', 'value']*=4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the raw data\n",
    "air_passengers_outlier_df.plot(x='time', y='value', figsize=(15,8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform the outlier data into `TimeSeriesData` Object\n",
    "air_passengers_outlier_ts = TimeSeriesData(air_passengers_outlier_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from kats.detectors.outlier import OutlierDetector\n",
    "\n",
    "ts_outlierDetection = OutlierDetector(air_passengers_outlier_ts, 'additive') # call OutlierDetector\n",
    "ts_outlierDetection.detector() # apply OutlierDetector"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Here we look at the outliers that the algorithum found."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Timestamp('1950-12-01 00:00:00'),\n",
       " Timestamp('1959-11-01 00:00:00'),\n",
       " Timestamp('1959-12-01 00:00:00')]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_outlierDetection.outliers[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After detecting the outlier, we can now easily removal them from the data. Here we will explore two options: \n",
    "- **No Interpolation**: outlier data points will be replaced with **NaN** values\n",
    "- **With Interpolation**: outlier data points will be replaced with **linear interploation** values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "air_passengers_ts_outliers_removed = ts_outlierDetection.remover(interpolate = False) # No interpolation\n",
    "air_passengers_ts_outliers_interpolated = ts_outlierDetection.remover(interpolate = True) # With interpolation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we visualize the difference between these two approaches to removing outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,8), nrows=1, ncols=2)\n",
    "\n",
    "air_passengers_ts_outliers_removed.to_dataframe().plot(x = 'time',y = 'y_0', ax= ax[0])\n",
    "ax[0].set_title(\"Outliers Removed : No interpolation\")\n",
    "air_passengers_ts_outliers_interpolated.to_dataframe().plot(x = 'time',y = 'y_0', ax= ax[1])\n",
    "ax[1].set_title(\"Outliers Removed : With interpolation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 An example with CUSUM algorithm\n",
    "\n",
    "Cusum is a method to detect an up/down shift of means in a time series. Our implementation has two main steps:\n",
    "\n",
    "1.  **Locate the change point:** This is an iterative process where we initialize a change point (in the middle of the time series) and CUSUM time series based on this change point.  The next changepoint is the location where the previous CUSUM time series is maximized (or minimized).  This iteration continues until either 1) a stable changepoint is found or 2) we exceed the limit number of iterations.\n",
    "\n",
    "2.  **Test the change point for statistical significance:** Conduct log likelihood ratio test to test if the mean of the time series changes at the changepoint calculated in Step 1.  The null hypothesis is that there is no change in mean.\n",
    "\n",
    "By default, we report a detected changepoint if and only if we reject the null hypothesis in Step 2.  \n",
    "\n",
    "Here are a few additional points worth mentioning:\n",
    "- We assume there is at most one increase change point and at most one decrease change point.  You can use the `change_directions` argument in the detector to specify whether you are looking an increase, a decrease, or both (default is both).\n",
    "- We use Gaussian distribution as the underlying model to calculate the CUSUM time series value and conduct the hypothesis test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import packages\n",
    "from kats.consts import TimeSeriesData, TimeSeriesIterator\n",
    "from kats.detectors.cusum_detection import CUSUMDetector\n",
    "\n",
    "# synthesize data with simulation\n",
    "np.random.seed(10)\n",
    "df = pd.DataFrame(\n",
    "    {\n",
    "        'time': pd.date_range('2019-01-01', '2019-03-01'),\n",
    "        'increase':np.concatenate([np.random.normal(1,0.2,30), np.random.normal(2,0.2,30)]),\n",
    "        'decrease':np.concatenate([np.random.normal(1,0.3,50), np.random.normal(0.5,0.3,10)]),\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is straightforward to use `CUSUMDetector` to detect an increase or a decrease when there is one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# detect increase\n",
    "timeseries = TimeSeriesData(\n",
    "    df.loc[:,['time','increase']]\n",
    ")\n",
    "detector = CUSUMDetector(timeseries)\n",
    "\n",
    "# run detector\n",
    "change_points = detector.detector(change_directions=[\"increase\"])\n",
    "\n",
    "# plot the results\n",
    "plt.xticks(rotation=45)\n",
    "detector.plot(change_points)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# detect decrease\n",
    "timeseries = TimeSeriesData(\n",
    "    df.loc[:,['time','decrease']]\n",
    ")\n",
    "detector = CUSUMDetector(timeseries)\n",
    "\n",
    "# run detector\n",
    "change_points = detector.detector(change_directions=[\"decrease\"])\n",
    "\n",
    "# plot the results\n",
    "plt.xticks(rotation=45)\n",
    "detector.plot(change_points)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we try to detect a decrease in a series where there is only an increase, no change point will be found."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:No change points detected!\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# detect increase\n",
    "timeseries = TimeSeriesData(\n",
    "    df.loc[:,['time','increase']]\n",
    ")\n",
    "detector = CUSUMDetector(timeseries)\n",
    "\n",
    "# run detector\n",
    "change_points = detector.detector(change_directions=[\"decrease\"])\n",
    "\n",
    "# plot the results\n",
    "plt.xticks(rotation=45)\n",
    "detector.plot(change_points)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we do not specify which change directions we are looking for, `CUSUMDetector` will look for both increases and decreases.  In the case below where there is only an increase, it will detect that increase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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39LCkKNP3GU/H3RcvJtkhrLY/u1DZvLQQ16gnJkdaqzFQlDnkLx7bww/eOBVwX13nINWFk/MFXu7cXM0zn7qcL928jmS/jlgR4V3nlvOHCaGi2rYBkh0yycBUFViPJ/YaHGnuI8khvgVYNi0uIMkhUQsVGWN4fGc9NYsLplzp7eYNFdR1Dk472fUXexr4/uun+ONLl/JP711PskN48XBrwGP3N/awLoQQkZfzF+Wz/++vDyms5M+FS6yhdbHYbzCtMRCRRSLykogcFJEDIvLJAMeIiPyXiBwXkbdFZJPfvg+LyDH79uFIn4CixCrHW/t4clcD//W7Y4yMjQ8L9LpG6RwYmdIzSHIIq8sChy1u8oaKDp4NFZ1o66e6MHPSKIW8jBRy0pInhYkON/extDiLtOQkwAp/rK/IjZox2Fvfw7HWft5/QWCvwMv1a8tISZo6VHS0pY/PP7GPmsUFfOGm1eSkp7B5aSEvHm6ZdGzP4ChnOodYXxHeF3t6SlJYx4M11mJpcRZvnYyNqi1/QvEMxoBPG2PWAhcDD4jI2gnH3AistG/3A98CEJFC4EvARcBm4EsiUhAh7YoS0zy92/qyau8f4fmD47+E6iZMKw2X86ryqMzPYKtfqKi2bcDXeTyRyoKMSY1nR1p6fSEiL5uXFrLnTPeUlTdzxeM7z5Ce4uCm88qnPC4vM4Urz3Hym31NAaex9rlG+diPdpKVlsw37t7kM45Xry7haEs/ZzrHfw777fLdc8P8lT9TLlxSwI7TnTE3SXZaY2CMaTLG7LLv9wGHgMoJh90C/MBYbAPyRaQcuB543hjTaYzpAp4HbojoGShKDGKM4ek9DVy6oojK/Ax+8tbpcfu9ZaUzXSRdRLjp3DL+cLydnsFR3B7DyY6BoOGVqoLxvQb9w2Oc6RxidelEY1DEiNszq8V1ZoJr1M0v9zRyw7qykOL2N2+ooKnHxc4J85u6Bkb485/t5XTHIA/etZFSv2qfq1eXANZkUn+88ft1ISSPI8GFSwrpHhzl+Bw1z82UsHIGIrIE2Ai8OWFXJeC/8kO9vS3Y9kCvfb+I7BCRHW1tbeHIUpSYY+fpLuq7hrhtUxV3XLiIPxzv4FT7gG//6c7Jo6vD5aZzyxl1G54/1EJj9xAjYx6WB/MM8jPG5QyO2qWZ50zwDC5cYjnu891v8MKhFnpdY7z/gkUhHX/NmlLSUxy+UFHnwAj/75nDXPavL/LCoRb++qY1XLysaNxzljmzWVqcNSlvsL+xl8r8jIDVS3NBrOYNQjYGIpINPAF8yhjTG2khxpiHjDE1xpgap9MZ6ZdXlHnlqd0NZKQkcf26Mm6/cBFJDuHRt+p8++s6BinKSvU1VM2E8xfl+0JFx4NUEnmpKsikb3iMnqFRAI7alUQTq2HyM1NZXZbDm/OcN3h8Zz0Veelcsrxo+oOBrLRk3rm6lK37mvhX2wh86+UTbFldwrOfuoI/vmxpwOddtaqE1090jFtj+kBDT9DhdHPB4qJMnDlp/P5IW0ytixySMRCRFCxD8GNjzJMBDmkA/E16lb0t2HZFSVhGxjz8Zl8T160rJSstmdLcdK5ZU8LPd9b7ZgpNV1YaCiLCjevLePVYG3vtoXYTewy8nC0vtcJTh5v7yExNYlHBZA2blxay83RX0Jr8SNPc4+KVo228b1MVSX6jvKfj5g3ltPeP8O2XT3C1bQS+cdemSXkQf965poSRMQ+vH7c8nz7XKLXtA2Enj2eDiPCBC6p4/mAL//HCsXl73+kIpZpIgO8Ch4wxXw9y2C+BD9lVRRcDPcaYJuBZ4DoRKbATx9fZ2xQlYXn5aBvdg6Pcev7ZiOhdFy2mc2CEZ+1GsUDTSmfCTedZoaIfvHGavIwUCoOEOqomjLI+0tzHytKccesoeNm8tJDBEbevEStSbN3XxGPbz0xquPL1FkxTRTSRa9aU8g+3rOO5T13Bg3dt4pzS6Wv+L1xSSHZaMr+zQ0UH7XMMt0R0tnzmulXcXlPFf/7uGA++GBsGIRQf9VLgXmCfiOyxt/0VUA1gjPk2sBW4CTgODAIfsfd1isiXge328/7BGBNbgTJFCROPx/DrfU1cs6Yk4ITLp3c3UJSVymUri33bLl9RzKLCDH7y5mmuX1dKY89QwAF14bJxUT4Veek09rjYVJ3vW6ZxIt6RF/X2jKIjLX1cu6Y04LGbl5xdwH2DvdDObOkZHOUvHtuDa9TDgy8d55PvXMmtGytxiFVFVLO4gKVBvJpgJCc5+NAlS8J6Tmqyg8tXFvPS4VZrZTPbGKybxzARWEth/vP7zmPMbfjac0dJTnLwsSuXz6uGSZqmO8AY85oxRowx5xljzrdvW40x37YNAXYV0QPGmOXGmHONMTv8nv+IMWaFffveXJ6MoswHBxp7+b+P7uYTP9k9KZTS6xrl+UMt3LyhYly9v8Mh3HFhNdtqO3nlaDvGMOswEdihIns0wlSNWoVZqWSkJNHQPURb/zCdAyNBwyklueksLc6KaN7giV31uEY9/O2715KTnsynf76X6/79Zf77xeOcaBuYtrcgkly9uoTmXhcHm3o50NBDaW4aJTmhzxiKFEkO4asf2MDNGyr4l98e5uFXa+ddgz/agawoYdJld/2+eLiVv/vVgXFJwGf2NTMy5uHWjZOL5j5QU0WyQ/jqs4eByBgDsKqK4OyCNoEQEV+vwZEgyWN/Ni8pZPupTtwRqIU3xvCjbafZWJ3PRy9byq8+cRnfunsTDhG+/vxR0lMcvGua3oJIsmWVVWL64qFW9jf2zGu+YCJJDuHfb9/AjevL+MffHOJ/Xz8VNS1qDBQlTPrsZSKvWVPCj7bV8fCrJ337nt7TwNLiLDZUTf6CKclJ57p1pRxtsSp/phpFEQ6bqvP5l/edywdqpv517V3k5siEmUSB2LLKSc/QKC8fDTy+IRxeP9FBbfsA9168GLC8pBvPLeeZT13Bf925kf/44EZyQpwJFAmcOWlsWJTPb/Y1cby1P6wxFHNBcpKD/7pzI9euLeV/Xj4RtUGBM69rU5QFSp/LKs/8u/esIy05iX/aeojKggw2VufzRm0Hn3rnOUFj93dtXszWfc1kpiZRnB2ZunYR4Y7N1dMeV5mfwZ4z3Rxu7qM4O42i7LSgx16zthRnTho/fOM0V68OnFsIlR++cZqCzBSfB+MlySG8Z0PFrF57ply9qoR/f+EowLQL2swHKUkOHrxrIx39I7MqN54N6hkoSph4PYPcjBT+7fYNXLC4gD//2R6+svUwxsAt5wf/gnvH8iIWF2WypCgrqMGYK6oKMukeHGVXXde00zZTkhzcubma3x9t843OCESfa5TP/Hwv+4J0LDf3uHj+UAu3X7hoRrN85op3rinx3Z/vSqJgpCUnURHmSOxIosZAUcKkzzWKCGSnJpOeksR3PlRDeV46v9rbyMbqfJZMURXjcAjfvucC/vW28+ZRsYW316C2bWDKEJGXuzZX4xDhx2+eDnrMt18+weM767n/hzvo6B+etP/Rt+rwGMPdmxfPXPgcsK4il5KcNAqzUsNaujKRUWOgKGHS6xojOy3ZV6NfmJXK9z6ymWXOLO67bNm0z19Tnsu5AXIKc4231wCmzhd4KctL57q1pfxsx5mAg+uae1x897WTXLikgI6BET750z3jEs6jbg+PvlXHlnOcM57BNFeICH/2zpXcd/nSeffQYhU1BooSJr2u0UnD1JYWZ/Hip7fMa1VMuFT5hSBCXZTl3osX0z04yq/fnrwI/X+8cBS3x/D128/ny7es47Xj7fz780d9+58/2EJr3zD3XBxbXoGXey9ezJ9uWRFtGTGDGgNFCZM+1xg56fFXe1GcnUZqsgMRWFkSmjG4ZHkRy51Z/HDb+FDRsZY+HttxhnsuXsyiwkw+eGE1t9dU8eBLx3nBHtf9o22nqczP8JVyKrGNGgNFCZM+12hcGgOHQ6jMz2BJURYZqaElc0WEey9ezN4z3bxd3+3b/q/PHCErNZk/u3qlb9s/3LKedRW5/Plje3jxcAuvn+jg7ourw5o3pEQPNQaKEiaWZzB/dfGR5P0XWCO1w+F9F1SRmZrED9+wvIPtpzp54VALH9uyfNwspPSUJL59zwUIcN//7iA1ycHtNeG9lxI91BgoSphYOYP48wwAHrhqBX8S5gyc3PQUbt1YyS/3NtI1MMJXth6iNDeNP7508pjoRYWZ/Mcd5+MxcNO5ZRRP0cugxBZqDBQlTOLZM5gp91y0mOExDw/8ZBe767r5i2vPCRpqunp1KU8/cClfvnX9PKtUZkN8/rxRlChhjInbBPJsWFuRS83iAl4/0cHKkmxu2zT16IvzIzTtVJk/1DNQlDAYGnXj9pgF5xkAfMQOC33+xtUkJ+lXR6KxsH7eKMos8Y6iWGieAVg5gFf+8qqYayBTIoOad0UJg157DeHcjIXnGYiIGoIERo2BooRB7wL2DJTEZtorWkQeAd4NtBpjJpUHiMhfAnf7vd4awGkveXkK6APcwJgxpiZSwhUlGnjHV8draamiBCMUz+D7wA3BdhpjvupdDhP4AvDyhHWOr7L3qyFQ4p6zOYOFFyZSEptQ1kB+BQh1MdQ7gUdnpUhRYhjfWgZqDJQEI2I5AxHJxPIgnvDbbIDnRGSniNw/zfPvF5EdIrKjra0tUrIUJaL02mEizRkoiUYkE8g3A3+YECK6zBizCbgReEBErgj2ZGPMQ8aYGmNMjdPpjKAsRYkcfa5RkhxCZoiD3hQlXoikMbiDCSEiY0yD/W8r8BSwOYLvpyjzTp+9sI0uiKIkGhExBiKSB1wJ/MJvW5aI5HjvA9cB+yPxfooSLRbiKAplYRBKaemjwBagWETqgS8BKQDGmG/bh70XeM4YM+D31FLgKfsXVDLwE2PMM5GTrijzT1+AVc4UJRGY1hgYY+4M4ZjvY5Wg+m+rBTbMVJiixCK9Q+oZKImJdiArShj0uka1x0BJSNQYKEoY9LnGtPtYSUjUGChKGMTr+seKMh1qDBQlRDweQ//w2IKcWKokPmoMFCVEBkbG8BjtPlYSEzUGihIiOqROSWTUGChKiCzkVc6UxEeNgaKESJ9vSJ16BkriocZAUULk7Phq9QyUxEONgaKESK96BkoCo8ZAUUKkVz0DJYFRY6AoIaI5AyWRUWOgKCHS5xoj2SGkp+ifjZJ46FWtKCHS5xolNyNFF7ZREhI1BooSIjq+Wklk1BgoSojokDolkVFjoCgh0ucaIydNk8dKYjKtMRCRR0SkVUQCrl8sIltEpEdE9ti3L/rtu0FEjojIcRH5fCSFK8p8o+sfK4lMKJ7B94EbpjnmVWPM+fbtHwBEJAn4BnAjsBa4U0TWzkasokQTbwJZURKRaY2BMeYVoHMGr70ZOG6MqTXGjAA/BW6ZwesoSkygnoGSyEQqZ3CJiOwVkd+KyDp7WyVwxu+YentbQETkfhHZISI72traIiRLUSKD22PoGx7ThjMlYYmEMdgFLDbGbAD+G3h6Ji9ijHnIGFNjjKlxOp0RkKUokaN/WEdRKInNrI2BMabXGNNv398KpIhIMdAALPI7tMrepihhcaKtn5v+81Xa+oajpuHsKAo1BkpiMmtjICJlYrdkishm+zU7gO3AShFZKiKpwB3AL2f7fsrCY/vJTg429bKttiNqGs6Or9YwkZKYTPszR0QeBbYAxSJSD3wJSAEwxnwbeD/wcREZA4aAO4wxBhgTkU8AzwJJwCPGmANzchZKQtPYPQTAgcZebt5QERUNuuSlkuhMawyMMXdOs/9B4MEg+7YCW2cmTVEsGntcABxo7Imaht4hDRMpiY12ICsxj9cz2N/Qg+V0zj99w2oMlMRGjYES8zR2DyECXYOjNNlewnyjYSIl0VFjoMQ0xhgae1zULC4ALO8gGpw1BuoZKImJGgMlpukYGGFkzMPVq0txCOxv7I2Kjl7XKKnJDtJTkqLy/ooy16gxUGKapm4rLLTMmcVyZzYHo5RE7h0a04YzJaFRY6DENA128rgyP4N1Fbnsb4iOZ2CtZaD5AiVxUWOgxDRNPZYxKM9LZ31lHs29Ltr7578TWYfUKYmOGgMlpmnsHiIt2UFhVirrKvIAq/lsvulzjWr3sZLQqDFQYprGbhcV+RmICGsrcoHoVBSpZ6AkOmoMlJimsWeIivx0APIyUqguzIxKJ3Kvrn+sJDhqDJSYprF7iPK8DN/j9ZW5UQoT6VoGSmKjxkCJWUbdHlr7hqnIP2sM1lXkcbpjkF57pLQ/I2Me7nn4TZ7Z3xxRHcbA4IhbPQMloVFjoMQszT0ujIGKvHTftnV23uBgAO9g674mXjvezu8OtURUh9ueh6QJZCWRUWOgxCzeOUQTPQOYnEQ2xvDwa7WAtRhOJBnzWMZAPQMlkVFjoMQs3mml/sbAmZNGaW7apLzBmyc72d/QS2FWKifaBiI63dTt9gA6pE5JbNQYKDNmYHiMWx58jR2nOufk9Rt7vMYgfdz29RV5kyqKHn71JIVZqdx3+VJ6hkbpGBiJmA6vZ6DjKJRERo2BMmP2N/Swt76Ht+bKGHQPkZ+ZQmbq+C/hdZV5HG/tZ2jEDUBtWz+/O9zCPRcv9oWRatsGpnxtt8cwZv/inw63L0yknoGSuKgxUGbM4eY+AFp752Y8RGO3a1xZqZd1Fbl4DBxqtkJF3/vDKVIcDu69eDHLnVnA9HmDL/5iPx965K2QdHiNQW6GegZK4jKtMRCRR0SkVUT2B9l/t4i8LSL7ROR1Edngt++UvX2PiOyIpPCJ9A+P8Ymf7OKJnfVz+TaKH4earC/jtjmaFdTYPUTlhBARwPrKs2MpugdH+PnOM9y6sQJnThoVeRmkpzg40Tq1MXj9RAe767rxeKbPLahnoCwEQvEMvg/cMMX+k8CVxphzgS8DD03Yf5Ux5nxjTM3MJIZGVmoSR5r7+MG203P5Noofh2zPoG3OPIOhgJ5BRV46BZkpHGjo4cdv1uEa9fDRy5YB4HAIy4qzp/QM+ofHONUxwNCom5a+6VdO02oiZSEwrTEwxrwCBA0KG2NeN8Z02Q+3AVUR0hYWIsKdm6vZe6Y7YA26ElncHsNRrzGYA8+gf3iMXtfYuEoiLyLCuoo8dtd187+vn+LylcWsKsvx7V/mzOLEFDmDI829eIuNTrZPnVsA61zTUxykJGlUVUlcIn11fxT4rd9jAzwnIjtF5P6pnigi94vIDhHZ0dbWNqM3f9+mSlKTHfx0e92Mnh/rnGof4JJ//h1HW/qiLYXT9i/r3PRk2voibwyaugNXEnlZV5nLkZY+WvuGue/yZeP2LXdmU981iGvUHfC5/mWpoRoDDREpiU7EjIGIXIVlDD7nt/kyY8wm4EbgARG5ItjzjTEPGWNqjDE1TqdzRhryM1O5aX0ZT+1u8FWaJBK/2NNIU4+LPWe6oy3Flzy+bGUx/cNjDI6MRfT1GwM0nPmz3q4aWlmSzRUri8ftW16SjcfA6Y7BgM892NhLfmYKackOToVgDMY8RstKlYQnIsZARM4DHgZuMcZ0eLcbYxrsf1uBp4DNkXi/qbhjczV9rjF+s69prt9q3nn2gDVzx7sUZDQ51NSLQ+Ady60v4kh7B4EazvzZWJ1PskP4+JbliMi4fdNVFB1s6mVdRS5LirJC9Aw86hkoCc+sjYGIVANPAvcaY476bc8SkRzvfeA6IGBFUiS5aGkhy4qzePStxAoVnekc5KBdveNd/WsqTrUP0DM4eZhbpDjU1McyZzaLCjMBaI2wMWjqHsIhUJqTFnB/VUEm2//6Gt63aXKKallxNkDAiqIxt4fDzX2sLc9lSXFmSMZgzGM0eawkPKGUlj4KvAGsEpF6EfmoiHxMRD5mH/JFoAj45oQS0lLgNRHZC7wF/MYY88wcnMNEvdy5uZqdp7tiIrYeKbxeQXF2mi+EMhV3P/wmf/uLubO9h5t7WVOeizPb+rKOtGfQ0O2iNDed5CmStgVZqQG3Z6QmUZmfEdAzONE2wMiYh7UVuSwtzqauc3Da5jO3x+iQOiXhmfbnjjHmzmn23wfcF2B7LbBh8jPmnvdtquT/PXuYR9+q40s3r4uGhIjz7IFmVpflUF04/a9Z16ibhu4heoZGGRnzkJoc2TqBXtco9V1D3Lm5mpLcuTEGVllp4ORxKCxzZlEb4HM62GSNsVhXkcfImIdRt6Gx20V1UWbQ13J7jDacKQlPQtbKFWWncf26Mp7c1RC0oiSeaOsbZsfpLm5YX0ZFfoZvmmcw6rusMFL/8BjbajumPHYmHLGTx2vLcynITCXJIbSGUK8fDk09Q0HzBaGw3JnNidb+SQPrDjb2kprsYFlxFkvtcFJt+9QNalpNpCwEEtIYANy5uZqeodGIL3QSDV441IIxcP26Msrz0ukfHqMvwOIuXhq6h8Y9N9J4O49Xl+eQ5BCKslIj6hkYY2jscc3OGJRkMzDipmVCQ9zBpl5Wl+WQnORgabGVaJ7K0/IY8BhDTpp6Bkpik7DG4JJlRSwuykyIRPIz+5upLsxkdVkO5fYX5FTeQYPtGZxbmccLB1siOs4ZrORxXkYKZblWGKckNy2iCeSOgRFGxjzjFrUJl0AVRcYYDjb2srbcWiCnODuV7LTkKctL3R7v+Go1Bkpik7DGwOEQ7riwmjdPdkZ8sZP5pNc1yusn2rlhfRki4oujN3YHryiq7xok2SHcfVE1jT0uXxVSpLCSxzm+kk5ndlpEPQPvuZXPMkwE441BU4+LrsFR1tqrpYkIS4sD5xa86FwiZaGQsMYA4P0XVJHsEH62/Uy0pcyYlw63Muo2XL+uFMBnDKb0DLqHKM9P55q1pYjA8wcjFyryeAxHmvtYXZbr21aSkx5Rz6DR7qOonIUxKMlJIzstedwoa++YEu/SmQBLirM41RHcGPjWMshQY6AkNgltDJw5aWyszmdPXXe0pcyY5w60WOexqACA0tx0RM6OawhEfdcQlfkZFGensam6IKJ5g7rOQQZH3L5QC1ifc0f/sO9X9GzxeQazCBOJCMudWeM8g4NNvYjAKj9DtrQ4i4auIYbHAhcauHVInbJASGhjAFYHa3PvzCpdGrqH+MzP90Z81EKouEbdvHSklevWluJwWCGZlCQHJTlT9xo0dA1RVWCVSl6zppT9Db0hNaqFwuHms8ljLyW5aXgMdEZodbHG7iHSkh0UBukjCBVvRZGXg429LCnKItsvGby0OBOPsZr6AqHGQFkoJLwxKMtNp7nHNaMk6ndeqeXxnfVsP9U1/cFzwGvH2hkccXP9urJx28vzMoJ+uY+MeWjpc/lCLNeutcJLL0QoVHSwqQ+HwDmlZ42Bt/EsUuWlTT2W/oljJsJleUk2jT0uBoYtY36wqXecRwOcLS8NMuV0yC5N9ibLFSVRSXxjkJfOiNsT9q9W16ibp/c0AHA4wgnYUHn2QDM56clcvKxo3PaK/PSg84maeoYwBqoKLGOw3JnF0uIsnj/UGhFNh5t6WVqcRXpKkm+bMyeyjWfenMdsWeZXOtrrGqWuc9CXPPaytMg6JljeoGdolMzUZIqyA4/FUJREIeGNQSgJ10A8d7CF7sFRkhziq6ufT8bcHl441MI1a0ondRBbnkFgb8dbVlppGwMR4Zo1Jbxxon3K3oRQOdTcy+oJv65LcqzPOFJJ5KaeISoCLGoTLstLzlYUHbKTxxONQV5mCoVZqQF7DQaGx+hzjZGfqcljJfFJeGNQZn+ptISZN3hs+xkq8zO4YmUxh5rmf8bRWyc76Roc9VUR+VOel87QqJueoclf7t7u46r8s+MVrl1bxqjb8Oqx9nHHukbdfOqnu7nzoW0haepzjXKmc2hSqCWSnsHImIfWvuFZlZV6WVyUiUOseUTe8tp1E7QDLCkKPOLjjRMdGGPI00oiZQGQ+MYgN3zP4EznIK8db+f2mkWsrcjlRFt/0GqTueLnO+vJSUvmynNKJu3zduY2BggV1dvTPsv8KnE2VedTkJkyrsS0e3CEe7/7Jk/vaeSN2o6QvAbv4L/VfquKgTUYLictMovctPS6MIaAax+HS1pyEtWFmZxo6+dgYy/F2ak+w+XP0uLsgMbglWNtOEQ0eawsCBLeGDhz0khyCM1hGIOf7ziDCHygpoo15bmMeQzHp1lgPZJ0DYzwm31NvHdTJRmpSZP2l/lCX5OTyA1dQ5Tmpo8LLSUnObhqdQkvHm5lzO2hvmuQ93/7Dfae6eE2ewR0KKOcD9oe0sQwEVifcySMwdmy0tl7BnC2ouhgkzVlNVBSemlxJi29w75Es5dXjraRm5GCY5aJbEWJBxLeGCQ5hJKctJDLS90ew8931nPFSicV+Rmssb/4pgsVtfa5IuY9PLGrnpExD3ddVB1wvzeeHqi8tL5r0Jc89ufaNaX0DI3yv2+c5r3ffJ3WXhc//OhmPr7FWjIylC7tw0295KYnBxwTESlj0DTNCmfhsrwkm9r2AY619E/KF3jxVhT5J5HrOgY51TFIvoaIlAVCwhsDsBq1QvUMXjnWRlOPizsuXATAkqIs0lMcUyaRB4bHuPprL3P9v7/CGydmNyXUGMNP3qpjU3X+uC5ff5w5aSQ7JGDjWUP3UMDO3SvOcZKa5ODLvz5IikN4/OPv4KJlRVQXZpHkkKCllf4carKSx4F+XTtz0iJSWtowzdrH4bKsOIuRMQ8jbg/r7KUyJ7Kk2MqvnGo/22vwyjFrHe48TR4rC4QFYQzK89JDbrp6bPsZirJSeecaK3Gb5BBWleb4mq0CsfN0F/3DY3QNjnLnd7bx+SfeDpjcDYU3T3ZS2zbAXRctDnpMkkMozU2flAcZc3to6nH5Gs78yUpL5t0bytmwKJ8n//RSX59AarLDF1efCu8YionJYy+R8gzqu4YoyEwhMzUycXpvRREQVPuSIm8J6tnP4JWjbVTmZ4wroVWURGZBGIOyvPRJo4wD0d4/zPMHW3jfpspxMfc15bkcauoL2ri2rbaDJIfwu09fyZ9csYzHdpzhmq+/zDP7w1+H+Sdv1pGbnsy7zyuf8rjyvPRJw+pa+qyREJUBwkQA//aBDfzigUvHJZfB+vV8onVqz6C+a4iBEfek5LGXkpx0Bkbck+Lu4dLQPRRU/0zwDqxLTzk7snoiWWnJlOWmc9L2DEbdHl4/0cEV5zjRbIGyUAjJGIjIIyLSKiIB11EUi/8SkeMi8raIbPLb92EROWbfPhwp4eFQljv9GgAAT+1qYMxj+KAdIvKyuiyHzoGRoHX0b57s5NzKPIqz0/jCTWv4xQOX4cxO42M/2sVd39nG8wdbQprb0zkwwjP7m3nfpqppf5GWB1jkpt4eqRBswFuwjt7lJdmc7BiYUqN3hbBAyWOIXHlpQ9fguLLY2VKYlUpBZgqry3JJcgT/arfWQ7Y8gz1nuukfHuPKc4ojpkNRYp1QPYPvAzdMsf9GYKV9ux/4FoCIFAJfAi4CNgNfEpGCmYqdKd5fwlPlDYwx/HR7HRcsLmBFyfhfv2eTyJNDRYMjY+w90z2uS/jcqjx+8YlL+Zt3reFk+wD/5wc72PK1l3jolRNTLlL/+M4zjLg93B0kcexPRd7kMRveeHugBPJULHdacXVvw1ogDjT2kuSQKTwD2xj0z9wYGGMi7hkA/J8rlvFH71gy5TFLi7M51WEZ01eOtpHkEC5ZrsZAWTiEZAyMMa8AnVMccgvwA2OxDcgXkXLgeuB5Y0ynMaYLeJ6pjcqc4C1TnKrXYFddFyfaBvhgzaJJ+7yJ3EAVRbtOdzPmMVy0rHDc9pQkB/ddvoxXP3sV37x7E+V5GXxl62Eu+ucX+MdfH5y0HKfHY3j0rTNcuKSAlaWBv3DHn5M1ZqPDb8yG98s83EqcZQFm/0/kQGMvK5zZQT0Wr2fQGkI4LhidAyO4Rj2zGl0diD/dsoJbN1ZOeczS4kw6B0boHhzhlaNtnL8oX5vNlAVFpHIGlYD/ogH19rZg2ychIveLyA4R2dHW1hYhWRbexrOpykt/d6iVZIfwrgCx+rzMFCrzMwJ6Bt58Qc3iwA5PcpKDm84t57E/uYSt//dy3n1eBQ+/dpJbv/EHjreeNS5v1HZwsn0gaDnpRHwrnvk1ntV3DeHMSQs76RloIZiJ7G/oGbcOwER8nsEsKoq8nk2kPYNQ8JaX7q7r5u2GHq5Y6Zx3DYoSTWImgWyMecgYU2OMqXE6I/uHWJJrfVFNFSY61trPkuIssoKsdbumPCegMXjzZAfrK/NCWglrbUUuX/vABr73kQtp6xvm5v/+Az/bXmeVk75ZR35mCjeunzpx7MW34plflVSwstLpKMxKJT8zhRNBykvb+oZp7RsOWqcPUJCZSpJDZjWfyDdXKcKeQSgstctLf7jtNMbAFZovUBYYkTIGDYB/fKXK3hZs+7ySnpJEUVbqlGGiE239rHBmB92/uiyX2vaBceGdoRE3e850c/HSwqDPC8RVq0r47ScvZ2N1Pp97Yh8f/9EunjvYzG0hJI69+EJffhVFwRrOQmG5M5vaIJ7BgUYreby+MnCdPljLjBZnp84qgeybqxQFz2BRoTXH6KUjreRlpHBeVf68a1CUaBIpY/BL4EN2VdHFQI8xpgl4FrhORArsxPF19rZ5pzQ3PeiwupExD6c7BlleErj0EKwksnvCWIrddV2Mus2kEdOhUJKbzg8/ehF/ef0qnj/UwqjbcOfm0EJEAEVZqaQmOXwGzuMxNHa7ZhxisVYFC+wZHAgy8XMiJTnps0ogN3QPkZ2WHJVYfVpyEpUFGRgDl60onrLySFESkZA6e0TkUWALUCwi9VgVQikAxphvA1uBm4DjwCDwEXtfp4h8Gdhuv9Q/GGOmSkTPGeV56UFXBzttl1WuKAnuGayxV/Y62NTr+4W8rbYDh0DNkpkVSCU5hAeuWsGlK4o52d4/5ftPxOEQyvzOqb1/mBG3h6oZhliWObN5bEc9PUOjk76MDzT2UF2YSe40oTBnTlpYM6Am4l2uc7aL2syUpcXZnOkc0hCRsiAJyRgYY+6cZr8BHgiy7xHgkfClRZayvHR21QVescybOF3hDF7Fs9geS3HYr6Jo28nOkPMFU3H+onzOX5Qf9vPK89J9YaIzvhDLzGr0vUnk2rZ+NlaPN24HGnunTB57KclJY19Dz4zeHyLfcBYuy4qzeOVoG5dr8lhZgMRMAnmuKctNp2twdFJJJ+AL/SxzBg8TJTmEVWW5viSya9TNnrpuLgozXxBJKvwaz2ZbieM994mhol7XKKc7BkMyBs6cNDr6h0NqsAtEQ9dgVJLHXu69ZDFfee+5ERuSpyjxxMIxBnb1TaC8wfHWfiry0oNWEnlZW57DoeZejDHsrutmxO2ZUb4gUpTnWXkQt8dQ3zV19/F0VBdmkuyQSUlk7wph66ZIHnspyUnDY6BjIPy8Qa9rlF7XWFQ9g+XO7JBLexUl0VgwxmCqxrPjbf3jBpoFY3VZLt2Do7T0DrOttgMRqFkSPc+gPD+DMY+hvX+YBnvA23QGLRgpSQ6qiyYPrPMmj0P1DGBmjWfRLCtVFGUBGYOyvMC9Bh6P4UTrQEjJW/+xFG+e7GBdRW5Uu1S96wo0dg9FJN6+3Jk9KUy0v7EHZ06ab53jqXDOYiRFQxTLShVFWVDGwPqSmdiF3NTrYmjUHZIxWG1XFO05082uum4uWhq9EBH4r3jmor5raNYD3pY7szndMcCY2+PbdjDE5DHgMxgz6TWIZvexoigLyBhkpyWTk5Y8yTPwJo+XT9Fw5iU33RpL8fMdZxgZi26+APxWPOseoqFr9p7BMmcWo27jq0xyjbo51tofsjGYzeTShu4hUpMdFGdNXqNYUZS5Z8EYA7B+SU9c5OaEbQxCrfFfU55LY48LEdgcxXwBQH5mCukpDg429jI06p51iMW/vBTgaEsfbo9hfZAVwiaSnpJETnryzIyB3WPg0GYvRYkKC84YNE9Ibh5v6yc/M4WirNSQXmOtHSpaU5Yb9SURRYSKvAzeOmX18c02+brcV15qGYP9Dd7kcWjGAGa+/GX9DOcqKYoSGRaWMchNp3mCZ3C8tZ/lzuyQu169i7tMHFkdLcrz0/1m+swuZ5CfmUpRVqpv1bMDjT3kpCezqDD0L+mSGS5/6fUMFEWJDgvKGJTnpdPWNzwuQXqideoBdRO5YHEBOenJXL+ubC4kho23ZBYik3xd7sym1l7x60BjL2vLc8MaD+HMSQ/bGLhG3bT3D2slkaJEkQVlDErz0vGYs6WPXQMjdAyMhDUTqDQ3nX1/d33Uk8devOWlOREa8LbMHljn9hgON/dOOak0EM7stLDHWGslkaJEnwVlDMr9SjHBbyZRGMYg1vAuchOpL9Llzmw6B0bYeboL16gn5EoiLyW5aQyOuBkYHgv5OdpwpijRZ0EZg7Jcu9fANgbhlJXGKl4DF6kQi3dG0S/3WstOhJM8BsszAMLyDtQzUJTos7CMQQDPIC3ZEddfQt6cwWyTx168hnHrvmbSkh2+CqNQ8a4qF07eoKFriCSH+JYnVRRl/llQxqAgM4XUZIdvWN3x1n6WObPjeiGTqoIMMlOTWFUWfPx2uK+XmuSgc2CE1WU5JCeFd4nMpPGsoXuIstz0sN9LUZTIMbOpZnGKiFhrAHjDRG39nL9oZgvTxApZacn8/jNbKAyxT2I6kpMcLC7KtDqPw0wew9mRFOH0GtR3Dca1d6YoicCC+ylWavcauEbd1HcNhR0GiUVKIvyr2hsqCjd5DJCfkUKyQ2gJY3JpQ9fQjFdoUxQlMoT0DSIiN4jIERE5LiKfD7D/30Vkj307KiLdfvvcfvt+GUHtM6I8L53mXhe1bQMYE9+VRHOFN4kcbvIYrOU4V5XlsDvIqnITGXV7aO6d+drNiqJEhmnDRCKSBHwDuBaoB7aLyC+NMQe9xxhj/tzv+D8DNvq9xJAx5vyIKZ4lZXnptPQMc6zVWr5SjcFkrl9XxtGWft+6z+Fy2cpiHnntJP3DY2RPs75Cc48Lj9GyUkWJNqF4BpuB48aYWmPMCPBT4JYpjr8TeDQS4uaCstx0Rtwetp/qxCGwpCj+w0SRZsOifB7+cA1pyUkzev4VK52Mug1v1nZMe6yWlSpKbBCKMagEzvg9rre3TUJEFgNLgRf9NqeLyA4R2SYitwZ7ExG53z5uR1tbWwiyZoa3Lv8PxztYVJhJesrMvvCU4FywuID0FAevHmuf9lhtOFOU2CDSCeQ7gMeNMf6rzi82xtQAdwH/ISLLAz3RGPOQMabGGFPjdDojLOss3kVuTrYPhDWTSAmd9JQkNi8t4tVj0xt1r2egi9ArSnQJxRg0AIv8HlfZ2wJxBxNCRMaYBvvfWuD3jM8nzDv+jU2aL5g7rlhZzIm2ARq7h6Y8rr5rEGdOmnpoihJlQjEG24GVIrJURFKxvvAnVQWJyGqgAHjDb1uBiKTZ94uBS4GDE587nzhz0nxNZvE8hiLWuXyl5d29Nk2oqEHXMVCUmGBaY2CMGQM+ATwLHAIeM8YcEJF/EJH3+B16B/BTY4zx27YG2CEie4GXgH/xr0KKBkkOocTukl2unsGccU5pNiU5abwyTagoEst1Kooye0LqQDbGbAW2Ttj2xQmP/y7A814Hzp2FvjmhNNfqQtYw0dwhIly2spgXD7fi9piAIz88HkNjtytm1oZQlIXMgutABquMsSQnLSLz/5XgXLHSSffgKAcaewLub+8fZsTtUc9AUWKABTWbyMtfXreKjoHwl2ZUwuPSFcUAvHqsnfOq8iftr+/2LtepxkBRos2C9AyWFGdxweLYWMM4kXHmpLGmPDdoiWm9r8cgMuO3FUWZOQvSGCjzxxUri9l5uivgyme+hjP1DBQl6qgxUOaUy+3RFG+d7By33RjD7rou8jJSpp1fpCjK3KPGQJlTapYUkJbsGFdi6vEY/ubp/Tx3sIW7L6qOojpFUbzoTzJlTrFGUxT65hSNuT189om3eXJXAx/fspy/vH5VlBUqigLqGSjzwBUrnRxv7aeuY5BP/nQPT+5q4NPXnsNnr1+FSPwuOaooiYR6Bsqcc/k5xbAVPvjQGzT1uPibd63hvsuXRVuWoih+qDFQ5pxVpTk4c9Jo6nHxj7eu556LF0dbkqIoE1BjoMw5IsLXPrABjzFctaok2nIURQmAGgNlXrjynLlbo0JRlNmjCWRFURRFjYGiKIqixkBRFEVBjYGiKIqCGgNFURQFNQaKoigKagwURVEU1BgoiqIogBhjoq1hEiLSBpye4dOLgfYIypktqmdqYk3PbIm181E9U5NIehYbY2bc3RmTxmA2iMgOY0xNtHV4UT1TE2t6ZkusnY/qmRrVcxYNEymKoihqDBRFUZTENAYPRVvABFTP1MSantkSa+ejeqZG9dgkXM5AURRFCZ9E9AwURVGUMFFjoCiKoqgxiAdEV42fEv185hb9fKcmUT4fNQY2IhJTn4WILBKR5QDGGBPtC05ELhaRG6KpwR8RKRaRPPB9PjH1/xcusaZfr7+pSbTrD9QYACAiVwN3iUhBtLUAiMhNwG+Bb4jIbyG6f5Aicj3wLSZ0RkZRz43AVuA7IvI4gDHGEw0tkUCvv2n16PU3vaYrROSyWRklY8yCvgGXAh7geeCDQEGU9WwE3gYusR8/CuRHUc8WrD/CjfbjTOwqNPuxzLOeq4DDwLVAHvAM8PloX0ezOB+9/qbWo9ff9JqutK+hN4BLAMdMXid5xlYkARCRZKAA64/QALcADhF5xhjTZR8jxv7E5wkP8JIx5g0RqQKuBr4qIk7gLmPM4Hxpsn95FQO1QIqIZAPfB4bs+3caY1zzocfWkgqsBb5gjHne3v5d4Py5fO+5Qq+/qdHrLyRdqcAK4DagHPgi8I8i8oYJ01tZ0MbAGDMmIi8AScaYARFJA27E+oP8rTGmc57/EAGGgdUi8t/Au4CvAw9i/RE8BVw/X5qMMcYOE4wA/wxsAP4JeNLW9RRw43zosd9j2HbLk/2+AJqBi+f6/ecCvf6mRq+/kHSNiMgvAJcxpl9EMoG/Bv5ZRF43xrhDfa0FaQxE5ErgMmAXcMwYcxzAGPNjO+Z2PdAqIhux3PYvzIOeS4HdwDbgo1hWvhD4pjFmAPiAiGwVEacxpm2O9Sw2xpwGsL+kfgdkAEXGmG/ax3wQeEJEco0xvXOs51KsP7gjwG5jTIPf7iEg2z7uPqDaGPPFudQzW/T6m1aPXn/Ta7oYOA94E2g0xvQDGGO+ZnsxXwD+r4hcjnUNfX2611xwCWQRuRZ4BCv2eC3wkIhs8e43xvwQ+CHwNeDPgMfmSU8W1pfAE8AKY8x2YAC4wD7udqAE61fSXOp5D3BSRP7Wu83+Mnga+B+/Q+8CirDCG3Op5932+1ZhhVE+KiLpfsnDWmCviHwAuB94fC71zBa9/qbVo9ff9JreBfwUK7/zaeD/iYhv0qkx5qtY180LwN8Bz4X0wnOR0IjlG/BJ4LP2/VzgHuAAsMXvmPcCfcDaKOrZALwb2I/15bAbWD/HWkqA/8X6VbETKzY68RgHcK+ta90c61kJ7AA224/fiZVoLfQ7pggrzn0QWBPt60uvP73+5uH/7O+Au+37q4BPAL8BLvA75lagJ5xraM6Fx9oNy3r/74Rt9wDPAsvsx9fOxx/iFHo+ZOvJANbZf5jV86BFgHfY91faF/gXJhyTb/+xzvmFjxXGvAdI99v2NHCV3+NsrF9J50T72prF/7def0avvzB0fQX4jt/jYtsg/I99Px34o3CN5byIj6UbVkXAXuBrftsKsZJkl9uP561cbQo93/LqmScdk87Z7w/yr+zH1wAVzLB0LUw9jgmPk+x/nwRutu9vtv8Y0+dazzz8f+v1N3mbXn+BteUDrwCf8du23t8oefWGc1tQOQMRSTLGjGBVbFwkIl8HMMZ0AinY8VFjf5pR1uPw6pkPJp6ziCQbY45hxUlvE5Fngf8CUsw8NNgEeA/vtXoGaBKRm4F/BXKMMa651hMJ9PoLjl5/oSEiDmNMN/BZ4FIR+Zytdz/gBi60H4dcReR77Xm67mIG+yIbE5FS4JfAUaANKz76bmPM0QWuZ1LNtoj8PfAAVlx7fzT1iMgXsWLqI8B9xph986lnpnjPI1b+v2NYT0xcf8H0RPP6C6DlQiyDdAo4BtwHXGOMOTmT10/Y0lIRKTPGNE/Y5rAv/IuAMqxyutuwEmffmcsLP070eP8AaoANxpjvisgKYDXwzrn8QwxBz0ZjzHewPpu1WDH1E3OlZ7bY/6drgJPAPmNMp/1LPFr/3/Gixx2l6286PfN+/dlloedjeSOvGWPa/TStx2pYfA/wp1hVVbfO1BAAiZkzwMqke4A/CrDvEmAPcIPqmVLPO+3HAuTGgJ5r7cdpxHiyGOtX9T7gYayyzU8DyVH8/45HPfN5/YWiZ16vP6zQ3V6sX/6PYnV/e/ddbevdEtH3nK8LYB4vtAqsWumvY5XI3Tth/93ATd4LTfVMqSfsJNQc60mJ9vUVwvmss788vLN03otVgZIWpf/veNYzH9dfOHrm5foDzsWaM+StrPpb4HNYvQ45WONLPhDp/7M5P7H5vmE181xt378Kqynk3gDHzfmFpnriT08EzqcIq1wzyW/b80BNlD5f1RNHeuz3KsDuGcAqFT0B/Br4BvATwGnvi6jxnpeTm6cPsAwoC7B9i/0F8yH78RVAsepRPXNwPuUTtnl/Xf4WuNi+fz6Qp3pUTxBNZRO2vRf4sH2/EvgudmlrxN9/Pk5yHj7E24BXseaqfA5rmJb//quw6pV/guUSVqke1TOP5/MIsAx4n/1FU6J6VM8Umj47UZPfcQ8TwHOOxC3uS0tFpAhrBscfA6NY3ZursMbw/szvuAexYm1XmzksB1M98aVntoRyPnb9/kasBqWPmLmtilE9caRnGk0vG2Me9TvuNuBvgNuMMbWR1pEIpaVJQC9w0hjTLSIdWJ2KW0SkzRjzooiswbL018zDF4vqiS89s2Wq8+kwxryA1dF7AbDJ2BNKVY/qCUHTFSLSYv9N3A/8OfD+uTAEkCBNZyLyn1hTFz9prJG3FVjzVUaMMV8Xa8Z3mrEXDFE9qieSTHE+o8aYfxORaiDbGHNQ9aieMDV5/ybKgMy5MgQQ5yOs5ex6n9/AsqyfE5EsY0wj1qCtW0SkyBgzOB9fLKonvvTMlhDO5z1izf+vm48vFtUTX3pC1HSLiBQbY5rn0hBAnBoDEWuWuDk7P+QE1gCpDODbIlIMnAOMYcXgVI/qiRhhns+w6lE9s9Q0p2tI+DTFU5hIRAqxlncb9NuWaqyl36qwYn0fxmoXLwQ+bozZpXpUTySItfNRPfGlJ1Y1+TBzXDIVqRvW9MJfYVnPe/FrCsFadOLn2DPXgTwgS/WonkQ9H9UTX3piVdM4ffP5ZrP4EM/BmsWxFqsJ6atY8zouxxr9uw2r3Er1qJ6EPx/VE196YlXTxFu8lJYWA/XGTuqISAHWpL7bscbt3mKMaRGZPP5W9aieCBBr56N64ktPrGoaR7wkkPcDvSLyN/bjjVhz113AEmNMC8zfoiCqJ+70zJZYOx/VE196YlXTOGI2gWwnU/qMMT0ikow1ouABLANmjDG3iMidwA1Yo4/n9ERUT3zpmS2xdj6qJ770xKqmKQk1njSfN6z59oeBv8Ce0Gdvd2ANc/KuR3o/8J+qR/Uk8vmonvjSE6uaprvFnGcgIk6shZ3rgHqgFfipMaZtwnGfAj4C3GPmdpaO6okjPbMl1s5H9cSXnljVFAqxmDPoAT4FfAxrYuUK4A4RKYGzzRpY8zzunocPUfXEl57ZEmvno3riS0+sapqWmPEMxJoH0oy13Jx/Q8ZtwJXAMWPMf4vIJjMPTRiqJ770zJZYOx/VE196YlVTOMSEZyAi7wK2Ag8C3xOR1d59xpgngJcBp4g8DbwsIpWqR/VEilg7H9UTX3piVVPYRDNhAQiwCHtxZ6AU+AzQBKybcOyPgFPAuapH9STi+aie+NITq5pmfC5RF2DFzR7CWtLNG7b6JNAAnGM/Lsda+ep81aN6Evl8VE986YlVTTM6j6i9sZVUuRBrQeqfAZ+dsP+zwPeBDPtxtupRPYl6PqonvvTEqqZZnU9U3hTeDbyNFUd7EHgPlvv0Bb9jlgD/g21pVY/qSdTzUT3xpSdWNc32Nu+ziUTkHVhDmu4yxuwWkYeAzcA7gG0ikoRVo3sZ1tJz+UCX6lE9kSDWzkf1xJeeWNUUEebb+mB9YH/k99gJ/Ma+vwx4BPgmsJN5SLSonvjSk2jno3riS0+saorIec37G1rJlly/+1XAbqDc3rYYSAbyVI/qSfTzUT3xpSdWNUXiNu99BsYYtzGm134oQDfQaYxpEpF7gL8CUowxPapH9USaWDsf1RNfemJVUySIiQ5kEfk+Vl3udVjuV1Tbs1VPfOmZLbF2PqonvvRAbGoKl6gaA3tGRwpwyP73ncaYY6pH9cwHsXY+qie+9MSqppkSK57BHwHbjTEHoq0FVM90xJqe2RJr56N6pibW9EBsagqXWDEGMbX8oeqZmljTM1ti7XxUz9TEmh6ITU3hEhPGQFEURYkuMTG1VFEURYkuagwURVEUNQaKoiiKGgNFURQFNQaKoigKagwURVEU4P8D3p2FR07Ij64AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# detect increase\n",
    "timeseries = TimeSeriesData(\n",
    "    df.loc[:,['time','increase']]\n",
    ")\n",
    "detector = CUSUMDetector(timeseries)\n",
    "\n",
    "# run detector\n",
    "change_points = detector.detector()\n",
    "\n",
    "# plot the results\n",
    "plt.xticks(rotation=45)\n",
    "detector.plot(change_points)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Feature Extraction with Kats\n",
    "\n",
    "We provide the `TsFeatures` module to calculate a set of meaningful features for a time series, including:\n",
    "- STL (Seasonal and Trend decomposition using Loess) Features\n",
    "    - Strength of Seasonality\n",
    "    - Strength of Trend\n",
    "    - Spikiness\n",
    "    - Linearity\n",
    "- Amount of Level Shift\n",
    "- Presence of Flat Segments\n",
    "- ACF and PACF Features\n",
    "- Hurst Exponent\n",
    "- ARCH Statistic\n",
    "\n",
    "Given a collection of time series, these features can be used to identify specific series that are similar or outlying.  Our `TsFeatures` module is similar to the one that is [freely available in R](https://pkg.robjhyndman.com/tsfeatures/index.html).\n",
    "\n",
    "These features also play a crucial role in many downstream projects, including \n",
    "1. “Meta-learning”, i.e., choosing the best forecasting model based on characteristics of the input time series \n",
    "2. Time series classification and clustering analysis\n",
    "3. Nowcasting algorithms for better short-term forecasting\n",
    "\n",
    "Now we will show you how to use `TsFeatures` get the features for the `air_passenger` data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initiate feature extraction class\n",
    "from kats.tsfeatures.tsfeatures import TsFeatures\n",
    "tsFeatures = TsFeatures()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_air_passengers = TsFeatures().transform(air_passengers_ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can see the dictionary of features as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'length': 144,\n",
       " 'mean': 280.2986111111111,\n",
       " 'var': 14291.97333140432,\n",
       " 'entropy': 0.4287365561752448,\n",
       " 'lumpiness': 3041164.5629058965,\n",
       " 'stability': 12303.627266589507,\n",
       " 'flat_spots': 2,\n",
       " 'hurst': -0.08023291030513482,\n",
       " 'std1st_der': 27.206287853461966,\n",
       " 'crossing_points': 7,\n",
       " 'binarize_mean': 0.4444444444444444,\n",
       " 'unitroot_kpss': 0.12847508180149078,\n",
       " 'heterogeneity': 126.06450625819339,\n",
       " 'histogram_mode': 155.8,\n",
       " 'linearity': 0.853638165603188,\n",
       " 'trend_strength': 0.9383301875692747,\n",
       " 'seasonality_strength': 0.3299338017939569,\n",
       " 'spikiness': 111.69732482853489,\n",
       " 'peak': 6,\n",
       " 'trough': 3,\n",
       " 'level_shift_idx': 118,\n",
       " 'level_shift_size': 15.599999999999966,\n",
       " 'y_acf1': 0.9480473407524915,\n",
       " 'y_acf5': 3.392072131604336,\n",
       " 'diff1y_acf1': 0.30285525815216935,\n",
       " 'diff1y_acf5': 0.2594591065999471,\n",
       " 'diff2y_acf1': -0.19100586757092733,\n",
       " 'diff2y_acf5': 0.13420736423784568,\n",
       " 'y_pacf5': 1.003288249401529,\n",
       " 'diff1y_pacf5': 0.2194123478008142,\n",
       " 'diff2y_pacf5': 0.2610103428699484,\n",
       " 'seas_acf1': 0.6629043863684492,\n",
       " 'seas_pacf1': 0.15616955255589093,\n",
       " 'firstmin_ac': 8,\n",
       " 'firstzero_ac': 52,\n",
       " 'holt_alpha': 0.9950208833037283,\n",
       " 'holt_beta': nan,\n",
       " 'hw_alpha': 0.9999999850988388,\n",
       " 'hw_beta': nan,\n",
       " 'hw_gamma': nan}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# features_air_passengers\n",
    "features_air_passengers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Summary\n",
    "\n",
    "in this tutorial, we have shown the basic operations of **Kats** in time series application, including basic introductions to forecasting, detection, feature extraction in Kats.\n",
    "\n",
    "If you want to explore these topics in more detail, you can check out Kats 20x tutorials:\n",
    "\n",
    "- [Kats 201 Forecasting with Kats](kats_201_forecasting.ipynb)\n",
    "- [Kats 202 Detection with Kats](kats_202_detection.ipynb)\n",
    "- [Kats 203 Time Series Features](kats_203_tsfeatures.ipynb)\n",
    "- [Kats 204 Forecasting with Meta-Learning](kats_204_metalearning.ipynb)"
   ]
  }
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